diff --git "a/xresnet50_ep_20_model_evaluation.html" "b/xresnet50_ep_20_model_evaluation.html"
--- "a/xresnet50_ep_20_model_evaluation.html"
+++ "b/xresnet50_ep_20_model_evaluation.html"
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-{"version_major": 2, "version_minor": 0, "state": {"1c2853e03bfc4b6197f302b5eb2b1bba": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "Model Evaluation Suite"}, "children": ["IPY_MODEL_8534947fd8244ce6b291e1c948103c20"], "layout": "IPY_MODEL_e4653a8ca9044696a53c995c43041902"}}, "8534947fd8244ce6b291e1c948103c20": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_62834daa973948eb9ea914fa446ce967", "IPY_MODEL_f1432d0417a84f07a2d1364219d445bb", "IPY_MODEL_5d8809e7ceb94d16b6dda70c9a52262c", "IPY_MODEL_16e330b257d9493c88999bac563451d3", "IPY_MODEL_1b9a43b602934f6fb7e882376395d0dd"], "layout": "IPY_MODEL_b27a065eaaa146e4bd8ebb224aba5b62"}}, "62834daa973948eb9ea914fa446ce967": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2aefa41ef13645b8b12e2dc716d9abc4", "style": "IPY_MODEL_d13ada13998b4421afda884f7eb6f0a1", "value": "\n<h1 id=\"summary_KF5B1SA3Y5T37SZPF9S9EHWHG\">Model Evaluation Suite</h1>\n<p>\n    The suite is composed of various checks such as: Mean Average Recall Report, Weak Segments Performance, Class Performance, etc...<br>\n    Each check may contain conditions (which will result in pass <span style=\"color: green;display:inline-block\">\u2713</span> /\n    fail <span style=\"color: red;display:inline-block\">\u2716</span> / warning <span style=\"color: orange;font-weight:bold;display:inline-block\">!</span> / error <span style=\"color: firebrick;font-weight:bold;display:inline-block\">\u2048</span>) as well as\n    other outputs such as plots or tables.<br>\n    Suites, checks and conditions can all be modified. Read more about\n    <a href=https://docs.deepchecks.com/stable/general/usage/customizations/auto_examples/plot_create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n</p>\n\n"}}, "2aefa41ef13645b8b12e2dc716d9abc4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d13ada13998b4421afda884f7eb6f0a1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f1432d0417a84f07a2d1364219d445bb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3d05fd340b57473d921ee7e8796b3569", "IPY_MODEL_c2d9ed3e7f2440e5a9c3211c8ef52d56"], "layout": "IPY_MODEL_376b57beba0e4699b158362dfda5e595"}}, "3d05fd340b57473d921ee7e8796b3569": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1462acaec3d24eb89ae5104a40c1bd4b", "style": "IPY_MODEL_b4c3cf06d6034ce99e08f1be325702d8", "value": "<span id=\"summary_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\"></span>"}}, "1462acaec3d24eb89ae5104a40c1bd4b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b4c3cf06d6034ce99e08f1be325702d8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c2d9ed3e7f2440e5a9c3211c8ef52d56": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Didn't Pass"}, "children": ["IPY_MODEL_276d6e1460854075b3445c4172c51fac"], "layout": "IPY_MODEL_47111e0d5976420ab48f688df0be3142", "selected_index": null}}, "276d6e1460854075b3445c4172c51fac": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6aac1e092b534a688126bf7db70049e2", "IPY_MODEL_38109e0bda524c74ac417b460085711a", "IPY_MODEL_8c01a469b8de44789edd2db5c0428870", "IPY_MODEL_b90bbd1b8c7b492e8602b494e336518d", "IPY_MODEL_62bd217e786249cb81939dac437c3248", "IPY_MODEL_b90bbd1b8c7b492e8602b494e336518d", "IPY_MODEL_9437ecd9abbe45398da002d91f2cd300", "IPY_MODEL_b90bbd1b8c7b492e8602b494e336518d", "IPY_MODEL_72c4bf5913384b9eae6361a66b6410ba"], "layout": "IPY_MODEL_4c5fd07a27d44db0a54a40f49e035942"}}, "6aac1e092b534a688126bf7db70049e2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cca0ee11aa3642dab34050263b78a864", "style": "IPY_MODEL_84ef09f14b354cada799f2155856ca2e", "value": "<style type=\"text/css\">\n#T_e4cde table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e4cde thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e4cde tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e4cde th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e4cde td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e4cde\">\n  <thead>\n    <tr>\n      <th id=\"T_e4cde_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_e4cde_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_e4cde_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_e4cde_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_e4cde_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_e4cde_row0_col1\" class=\"data row0 col1\" ><a href=\"#ClassPerformance_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\">Class Performance</a></td>\n      <td id=\"T_e4cde_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_e4cde_row0_col3\" class=\"data row0 col3\" >3 scores failed. Found max degradation of 18.32% for metric Recall and class culex_tritaeniorhynchus.</td>\n    </tr>\n    <tr>\n      <td id=\"T_e4cde_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_e4cde_row1_col1\" class=\"data row1 col1\" ><a href=\"#SimpleModelComparison_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\">Simple Model Comparison</a></td>\n      <td id=\"T_e4cde_row1_col2\" class=\"data row1 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_e4cde_row1_col3\" class=\"data row1 col3\" >Found metrics with gain below threshold: {'F1': {'culex_tritaeniorhynchus': '9.42%'}}</td>\n    </tr>\n    <tr>\n      <td id=\"T_e4cde_row2_col0\" class=\"data row2 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_e4cde_row2_col1\" class=\"data row2 col1\" ><a href=\"#WeakSegmentsPerformance_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\">Weak Segments Performance</a></td>\n      <td id=\"T_e4cde_row2_col2\" class=\"data row2 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_e4cde_row2_col3\" class=\"data row2 col3\" >Found a segment with cross entropy of -4.85 in comparison to an average score of -1.153 in sampled data.</td>\n    </tr>\n    <tr>\n      <td id=\"T_e4cde_row3_col0\" class=\"data row3 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_e4cde_row3_col1\" class=\"data row3 col1\" ><a href=\"#WeakSegmentsPerformance_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\">Weak Segments Performance</a></td>\n      <td id=\"T_e4cde_row3_col2\" class=\"data row3 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_e4cde_row3_col3\" class=\"data row3 col3\" >Found a segment with cross entropy of -6.027 in comparison to an average score of -0.984 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "cca0ee11aa3642dab34050263b78a864": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "84ef09f14b354cada799f2155856ca2e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "38109e0bda524c74ac417b460085711a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4f52fefc548f408c9d34cbd7b953ef3d", "style": "IPY_MODEL_7de13193fd4b4736b59d2bf89664cf30", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "4f52fefc548f408c9d34cbd7b953ef3d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7de13193fd4b4736b59d2bf89664cf30": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8c01a469b8de44789edd2db5c0428870": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_a9aba95850bf472da63c306ea4a28ff7", "IPY_MODEL_4b0d9c1cc5aa463d89a5da6d6cfec7c3", "IPY_MODEL_bb680c6234cf43a7a5e9627025071405", "IPY_MODEL_32129801d428425f82a49e8d0201a149"], "layout": "IPY_MODEL_2a7ea6e50c9b42eda9f1b7a4f27ed05c"}}, "a9aba95850bf472da63c306ea4a28ff7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_655411ff33a84fd194f9338c73ef8f20", "style": "IPY_MODEL_dc33e28393e249d79826a1894d39fcec", "value": "<h4 id=\"ClassPerformance_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\"><b>Class Performance</b></h4>"}}, "655411ff33a84fd194f9338c73ef8f20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc33e28393e249d79826a1894d39fcec": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4b0d9c1cc5aa463d89a5da6d6cfec7c3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3522e662a3d84c3a8357f9d45129a039", "style": "IPY_MODEL_e025e54a06744c83b2cf87aacf138b86", "value": "<p>Summarize given metrics on a dataset and model. <a href=\"https://docs.deepchecks.com/0.17/vision/auto_checks/model_evaluation/plot_class_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "3522e662a3d84c3a8357f9d45129a039": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e025e54a06744c83b2cf87aacf138b86": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bb680c6234cf43a7a5e9627025071405": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a10d58f914714052803de4988de4ab1a", "style": "IPY_MODEL_09e17e270e6c43f888d656ef9508f097", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_4e947 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e947 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e947 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e947 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e947 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_4e947\">\n  <thead>\n    <tr>\n      <th id=\"T_4e947_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_4e947_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_4e947_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_4e947_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_4e947_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_4e947_row0_col2\" class=\"data row0 col2\" >3 scores failed. Found max degradation of 18.32% for metric Recall and class culex_tritaeniorhynchus.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a10d58f914714052803de4988de4ab1a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "09e17e270e6c43f888d656ef9508f097": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "32129801d428425f82a49e8d0201a149": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_67fd97499acb4b50a377d032a4e11c82", "IPY_MODEL_24fdffd05ec648eb9b45f6638052bf09", "IPY_MODEL_101ef29ec63c4b9d8133902b53bf541b"], "layout": "IPY_MODEL_da5ff40c69e34bea8d758e6d21fa6ab1"}}, "67fd97499acb4b50a377d032a4e11c82": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9cffcae85b844564ba91fa761b7ac3a5", "style": "IPY_MODEL_9a9297ef57264a07ae97adf0bb60839d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9cffcae85b844564ba91fa761b7ac3a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a9297ef57264a07ae97adf0bb60839d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "24fdffd05ec648eb9b45f6638052bf09": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.15.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Precision<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": true, "x": ["culex_tritaeniorhynchus", "anopheles_sinensis", "culex_pipiens", "aedes_albopictus", "aedes_vexans"], "xaxis": "x", "y": {"dtype": "float64", "shape": [5]}, "yaxis": "y", "type": "histogram", "uid": "d685393d-5907-416d-b2b5-df6213666a08"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": ["aedes_albopictus", "aedes_vexans", "culex_pipiens", "anopheles_sinensis", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [5]}, "yaxis": "y2", "type": "histogram", "uid": "049f52fa-9747-4ad4-8681-b9575ebd0b47"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Precision<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": true, "x": ["culex_tritaeniorhynchus", "culex_pipiens", "anopheles_sinensis", "aedes_albopictus", "aedes_vexans"], "xaxis": "x", "y": {"dtype": "float64", "shape": [5]}, "yaxis": "y", "type": "histogram", "uid": "3f64eb37-83ce-4961-b5ce-2fb661db877b"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": false, "x": ["aedes_albopictus", "aedes_vexans", "culex_pipiens", "anopheles_sinensis", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [5]}, "yaxis": "y2", "type": "histogram", "uid": 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"state": {}}, "5416c42baf8d4877a22c6ed1e2017111": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "da5ff40c69e34bea8d758e6d21fa6ab1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2a7ea6e50c9b42eda9f1b7a4f27ed05c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b90bbd1b8c7b492e8602b494e336518d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_afe793225d744f53885c667af59565bf", "style": "IPY_MODEL_2c8fc552d8c24de786793b669b79e705", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "afe793225d744f53885c667af59565bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2c8fc552d8c24de786793b669b79e705": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "62bd217e786249cb81939dac437c3248": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_cf48fbb1251b406f90bbc8e4d9a1ed6d", "IPY_MODEL_65e61d89ff65473f8ce4366556de5eca", "IPY_MODEL_63958082727f4f368579c74867dca7fd", "IPY_MODEL_58f3a9d42b1a4116a2eefae80c8e88de"], "layout": "IPY_MODEL_45e604dcd58a4222b07aecfdf5b3c6d5"}}, "cf48fbb1251b406f90bbc8e4d9a1ed6d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3cc1ee0aee4e40d09a5f7e0ef71b0e9a", "style": "IPY_MODEL_58bb9e63286344f191f0d959f0bb99f5", "value": "<h4 id=\"SimpleModelComparison_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\"><b>Simple Model Comparison</b></h4>"}}, "3cc1ee0aee4e40d09a5f7e0ef71b0e9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "58bb9e63286344f191f0d959f0bb99f5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "65e61d89ff65473f8ce4366556de5eca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a6284ecf66ec4c9781a08c2c37ee6c83", "style": "IPY_MODEL_0bf6332bf77c4408adbf01fd047fdebb", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.17/vision/auto_checks/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "a6284ecf66ec4c9781a08c2c37ee6c83": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0bf6332bf77c4408adbf01fd047fdebb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "63958082727f4f368579c74867dca7fd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bfb8ce89a48444c59bb0ad470114c0fa", "style": "IPY_MODEL_5bc821b264d44341bd9e7bf9fd483483", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_de1c1 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_de1c1 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_de1c1 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_de1c1 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_de1c1 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_de1c1\">\n  <thead>\n    <tr>\n      <th id=\"T_de1c1_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_de1c1_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_de1c1_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_de1c1_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_de1c1_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_de1c1_row0_col2\" class=\"data row0 col2\" >Found metrics with gain below threshold: {'F1': {'culex_tritaeniorhynchus': '9.42%'}}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "bfb8ce89a48444c59bb0ad470114c0fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5bc821b264d44341bd9e7bf9fd483483": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "58f3a9d42b1a4116a2eefae80c8e88de": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_fc17ae28751f45988711e4611337fdb3", "IPY_MODEL_407abd2b2578413e8aef077f19277b21", "IPY_MODEL_7e3375f88d0e4261958409883b0baa3e"], "layout": "IPY_MODEL_fc2840c6b60140b29a3e32dfe07f5328"}}, "fc17ae28751f45988711e4611337fdb3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1c2f5ce467064be68e6c84ae4b4abbe6", "style": "IPY_MODEL_86c68174cc2b482d8332ea3c8189bacd", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1c2f5ce467064be68e6c84ae4b4abbe6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "86c68174cc2b482d8332ea3c8189bacd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "407abd2b2578413e8aef077f19277b21": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.15.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Simple Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Simple Model", "marker": {"color": "#2191FB", "pattern": {"shape": ""}}, "name": "Simple Model", "offsetgroup": "Simple Model", "orientation": "v", "showlegend": true, "x": ["culex_tritaeniorhynchus", "aedes_albopictus", "aedes_vexans", "anopheles_sinensis", "culex_pipiens"], "xaxis": "x", "y": {"dtype": "float64", "shape": [5]}, "yaxis": "y", "type": 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"white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Simple Model (Strategy: most_frequent) vs. Given Model"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "k1NrwgHq0j8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "neVJZBq26z+gVJh4P3LqP////////+c/RnMZzWU05z+YcimXcinXPw=="}]}, "7e3375f88d0e4261958409883b0baa3e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a88c7d8bcd7f4209bba39b3c18228ccd", "style": "IPY_MODEL_161533142f824f8693ccb2f84af5e9e5", "value": "<br><a href=\"#summary_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\" style=\"font-size: 14px;\">Go to top</a>"}}, "a88c7d8bcd7f4209bba39b3c18228ccd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "161533142f824f8693ccb2f84af5e9e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fc2840c6b60140b29a3e32dfe07f5328": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "45e604dcd58a4222b07aecfdf5b3c6d5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9437ecd9abbe45398da002d91f2cd300": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_97fa79fda7014b28b39173cced418522", "IPY_MODEL_b984296c6c14497cb673d84be7c2034c", "IPY_MODEL_2d241576b7f4404fbf1602c674f32440", "IPY_MODEL_7068196c1eec4bd9bf9b9024c3fced45"], "layout": "IPY_MODEL_bde7decfe8f042a7bbdb1dfe305f9f5a"}}, "97fa79fda7014b28b39173cced418522": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8a870a551cf3421783d715525d3c4be5", "style": "IPY_MODEL_d138f565401f4312bc69d2815bcb4c26", "value": "<h4 id=\"WeakSegmentsPerformance_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\"><b>Weak Segments Performance</b></h4>"}}, "8a870a551cf3421783d715525d3c4be5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d138f565401f4312bc69d2815bcb4c26": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b984296c6c14497cb673d84be7c2034c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c2a9ad138f0b4c908ec50121f9151071", "style": "IPY_MODEL_cc0e1ecfabd94a5a8af862138fca9044", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.17/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c2a9ad138f0b4c908ec50121f9151071": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cc0e1ecfabd94a5a8af862138fca9044": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2d241576b7f4404fbf1602c674f32440": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5c02b877d8fd422590d36169fa5dcffb", "style": "IPY_MODEL_65f69015b6fc4691bd41dea056e68f7d", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_3aeb1 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3aeb1 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3aeb1 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3aeb1 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3aeb1 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3aeb1\">\n  <thead>\n    <tr>\n      <th id=\"T_3aeb1_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_3aeb1_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_3aeb1_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3aeb1_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_3aeb1_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_3aeb1_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -4.85 in comparison to an average score of -1.153 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5c02b877d8fd422590d36169fa5dcffb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "65f69015b6fc4691bd41dea056e68f7d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7068196c1eec4bd9bf9b9024c3fced45": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a1366a204474452c899148961a2d9a4a", "IPY_MODEL_90495ce02d5c4b27b4af8f77e8c36177", "IPY_MODEL_31460476b9194a79a388efc7a4fd1e91", "IPY_MODEL_7b827c360478490eb9294217b8085c72"], "layout": "IPY_MODEL_8e70c8e4f0234804b77fc8676928a082"}}, "a1366a204474452c899148961a2d9a4a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c7e5e12e1e9e4650bee24e4b2f8606a2", "style": "IPY_MODEL_a9cf9a82cf0e44c889c306e44ce0af75", "value": "<h5><b>Additional Outputs</b></h5>"}}, "c7e5e12e1e9e4650bee24e4b2f8606a2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a9cf9a82cf0e44c889c306e44ce0af75": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "90495ce02d5c4b27b4af8f77e8c36177": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_744763ae51104a9f92acd49ec7a3ceb8", "style": "IPY_MODEL_74bd45518fd749208b187ec21d558287", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "744763ae51104a9f92acd49ec7a3ceb8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "74bd45518fd749208b187ec21d558287": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "31460476b9194a79a388efc7a4fd1e91": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0d08bae0bce2455bba2eec7b5af689a6", "IPY_MODEL_4ac10a3c1ad344308ffa2de5691d377a"], "layout": "IPY_MODEL_462b7101c14b4e4884e10b6de84a4ac9"}}, "0d08bae0bce2455bba2eec7b5af689a6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6365b58359f446fa90fbc396cceaa028", "style": "IPY_MODEL_47a93a13195b4ef78e22b1d1ef19e588", "value": 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{"_dom_classes": [], "layout": "IPY_MODEL_09ab725db1a8467a9e32d0ff4638dd5b", "style": "IPY_MODEL_8d783d834e1b4e24b3511f402f566b53", "value": "<br><a href=\"#summary_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\" style=\"font-size: 14px;\">Go to top</a>"}}, "09ab725db1a8467a9e32d0ff4638dd5b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8d783d834e1b4e24b3511f402f566b53": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8e70c8e4f0234804b77fc8676928a082": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bde7decfe8f042a7bbdb1dfe305f9f5a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "72c4bf5913384b9eae6361a66b6410ba": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_07a5f355b03340df87660cef733134f7", "IPY_MODEL_8546fa73e8c04fe4817cf4f67c241f12", "IPY_MODEL_eb31d40bf6f84b7982205790a79e796d", "IPY_MODEL_f58578aeced6481f88ada93bf5f5041d"], "layout": "IPY_MODEL_4c6dbadf6a2b4e90b7471afadea6cee7"}}, "07a5f355b03340df87660cef733134f7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_db0af50275364262a39edeeac2be78f2", "style": "IPY_MODEL_429a583d39374e31b99a827f4f196fb2", "value": "<h4 id=\"WeakSegmentsPerformance_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\"><b>Weak Segments Performance</b></h4>"}}, "db0af50275364262a39edeeac2be78f2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "429a583d39374e31b99a827f4f196fb2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8546fa73e8c04fe4817cf4f67c241f12": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5f1423cb8b9c4d19b92c7676ca3a5bba", "style": "IPY_MODEL_c317a5a0bdb84b989fec2ff5b44b2b64", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.17/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5f1423cb8b9c4d19b92c7676ca3a5bba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c317a5a0bdb84b989fec2ff5b44b2b64": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eb31d40bf6f84b7982205790a79e796d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f1dfe4fa19a7479a96976180fc2f255e", "style": "IPY_MODEL_b56947ae46d84e8bb8cceb119f67e295", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_6bcc0 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6bcc0 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6bcc0 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6bcc0 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6bcc0 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6bcc0\">\n  <thead>\n    <tr>\n      <th id=\"T_6bcc0_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_6bcc0_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_6bcc0_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_6bcc0_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_6bcc0_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_6bcc0_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -6.027 in comparison to an average score of -0.984 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f1dfe4fa19a7479a96976180fc2f255e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b56947ae46d84e8bb8cceb119f67e295": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f58578aeced6481f88ada93bf5f5041d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0068001b5c864dcba7f94d973dbe4492", "IPY_MODEL_393b51edc44d42fa9e113a9ac1c39f32", "IPY_MODEL_4e39cbdb21764a96a09037dfe7596d20", "IPY_MODEL_2d32d4c7f399413189c2de5bbd43700f"], "layout": "IPY_MODEL_927035ef4508432da518c2fbf0e812dc"}}, "0068001b5c864dcba7f94d973dbe4492": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_85df107555534248b2bb7f834fa13a3e", "style": "IPY_MODEL_147d1484ef514c2fa20bbf252b4298ea", "value": "<h5><b>Additional Outputs</b></h5>"}}, "85df107555534248b2bb7f834fa13a3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "147d1484ef514c2fa20bbf252b4298ea": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "393b51edc44d42fa9e113a9ac1c39f32": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_99f6bb6fda594b7b923d3bfb68787b20", "style": "IPY_MODEL_cfceb84d8d0f49579dccc55b0a776e97", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "99f6bb6fda594b7b923d3bfb68787b20": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cfceb84d8d0f49579dccc55b0a776e97": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4e39cbdb21764a96a09037dfe7596d20": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_beb7573b17094ab2b8c9bfb5a170b08f", "IPY_MODEL_c8367c7a95164e3ea03c63a38cebd84f"], "layout": "IPY_MODEL_7e10a17016f944fab61255c4e6f0326e"}}, "beb7573b17094ab2b8c9bfb5a170b08f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_28eb96c09ca54dc682f15a5f6042b9fa", "style": "IPY_MODEL_3d2af31a401f4714ab5b74c5feb3d07a", "value": "<style>.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {min-width: fit-content;}</style>"}}, "28eb96c09ca54dc682f15a5f6042b9fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3d2af31a401f4714ab5b74c5feb3d07a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c8367c7a95164e3ea03c63a38cebd84f": {"model_name": "TabModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "RMS Contrast vs Mean Red Relative Intensity", "1": "Brightness vs RMS Contrast", "2": "Brightness vs Mean Red Relative Intensity"}, "children": ["IPY_MODEL_ca0bd2c8e2ec406ba3c78f2b2b370e13", "IPY_MODEL_dccc95315dbf4f20bac4948954179a6f", "IPY_MODEL_18ddc90e3f4e44d4b6ec04a918eddf8f"], "layout": "IPY_MODEL_4d736bd18bf84286ad555621d2e6130f"}}, "ca0bd2c8e2ec406ba3c78f2b2b370e13": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_9f97b398266e41beb1378aac59941e55", "IPY_MODEL_7b072633d2b740bb9c3b08627c94903e"], "layout": "IPY_MODEL_7cdb93318ddd4a92bd7c1f1a03a24378"}}, "9f97b398266e41beb1378aac59941e55": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.15.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"coloraxis": "coloraxis", 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"parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Cross Entropy (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "Brightness"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false, "title": {"text": "Mean Red Relative Intensity"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "be2f2c3b07d94fd28baf7c9340e14bf6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bb980bb2615c45aaaa56f369351a65bf", "style": "IPY_MODEL_808c5dd481024f45b7cbf6f8ed998b73", "value": "<div>Check ran on 1735 data samples. Cross Entropy on the full data set is -0.98.</div>"}}, "bb980bb2615c45aaaa56f369351a65bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "808c5dd481024f45b7cbf6f8ed998b73": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ef4587ee6c0e422697c14c3f2fdece6f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d736bd18bf84286ad555621d2e6130f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7e10a17016f944fab61255c4e6f0326e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2d32d4c7f399413189c2de5bbd43700f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c9839739e9a64ae589de08fa0eb7e1e5", "style": "IPY_MODEL_1afd85d0cfe04ae9b23f27f6219d4e36", "value": "<br><a href=\"#summary_KF5B1SA3Y5T37SZPF9S9EHWHG-section-0U43Q\" style=\"font-size: 14px;\">Go to top</a>"}}, "c9839739e9a64ae589de08fa0eb7e1e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1afd85d0cfe04ae9b23f27f6219d4e36": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "927035ef4508432da518c2fbf0e812dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4c6dbadf6a2b4e90b7471afadea6cee7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4c5fd07a27d44db0a54a40f49e035942": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "47111e0d5976420ab48f688df0be3142": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "376b57beba0e4699b158362dfda5e595": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d8809e7ceb94d16b6dda70c9a52262c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_662db7ea5b6c42b593116b7eaf5624aa", "IPY_MODEL_e5e8b397fef64631b20b0f92066e630a"], "layout": "IPY_MODEL_d13718259ccd4af587a9da8300a1e660"}}, "662db7ea5b6c42b593116b7eaf5624aa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c921634b92144e9ca81396667f9fab35", "style": "IPY_MODEL_54fe10f2a26e4d9eadd58f4f7ada31a6", "value": "<span id=\"summary_KF5B1SA3Y5T37SZPF9S9EHWHG-section-LX3F2\"></span>"}}, "c921634b92144e9ca81396667f9fab35": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54fe10f2a26e4d9eadd58f4f7ada31a6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e5e8b397fef64631b20b0f92066e630a": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Passed"}, "children": ["IPY_MODEL_f8c402fac9df427a922f8cdb765b7edc"], "layout": "IPY_MODEL_0d521c4084a341989d5571a6b53a4b4a", "selected_index": null}}, "f8c402fac9df427a922f8cdb765b7edc": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_f34f68d9b2ec4655993c3f83059babbd", "IPY_MODEL_7f8a493687e54a6b92a22a154b25af13", "IPY_MODEL_942c15c1baec4a61bcf5a39c3bf985f7"], "layout": "IPY_MODEL_df138d94a933460ca10e9b9357e00acd"}}, "f34f68d9b2ec4655993c3f83059babbd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7a9d2366e86d49f9bc0e64b9287dce13", "style": "IPY_MODEL_44ee8eba9a024b6e8c25bbb46d10fc51", "value": "<style type=\"text/css\">\n#T_edbfd table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_edbfd thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_edbfd tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_edbfd th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_edbfd td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_edbfd\">\n  <thead>\n    <tr>\n      <th id=\"T_edbfd_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_edbfd_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_edbfd_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_edbfd_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_edbfd_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_edbfd_row0_col1\" class=\"data row0 col1\" ><a href=\"#PredictionDrift_KF5B1SA3Y5T37SZPF9S9EHWHG-section-LX3F2\">Prediction Drift</a></td>\n      <td id=\"T_edbfd_row0_col2\" class=\"data row0 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_edbfd_row0_col3\" class=\"data row0 col3\" >Passed for 1 prediction properties out of 1 prediction properties.\nFound prediction property \"Samples Per Class\" has the highest categorical drift score: 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7a9d2366e86d49f9bc0e64b9287dce13": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "44ee8eba9a024b6e8c25bbb46d10fc51": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f8a493687e54a6b92a22a154b25af13": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_86f6f46ad9b8478c9de08b5da0be43a8", "style": "IPY_MODEL_f7e1df78513d440ab91c3a213f8e22f5", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "86f6f46ad9b8478c9de08b5da0be43a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f7e1df78513d440ab91c3a213f8e22f5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "942c15c1baec4a61bcf5a39c3bf985f7": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_d5753e39ef774684bd9ede3e7b43e501", "IPY_MODEL_706a834d97cd42f892faa56032e56b01", "IPY_MODEL_40e82f9340574090a1bdb9181c6c073a", "IPY_MODEL_d1868200ee3340378e46678b869431bf"], "layout": "IPY_MODEL_fe20260c38f54ebdabd9460ca5936e95"}}, "d5753e39ef774684bd9ede3e7b43e501": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_81876da778cc4a929cf7baf085059921", "style": "IPY_MODEL_53ba2cd002054e75840a117014945567", "value": "<h4 id=\"PredictionDrift_KF5B1SA3Y5T37SZPF9S9EHWHG-section-LX3F2\"><b>Prediction Drift</b></h4>"}}, "81876da778cc4a929cf7baf085059921": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "53ba2cd002054e75840a117014945567": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "706a834d97cd42f892faa56032e56b01": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b9131462608a4612ab9906ed0dddea50", "style": "IPY_MODEL_2830a5e5bcb64d699ca0eb4d6ee52bca", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.17/vision/auto_checks/model_evaluation/plot_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "b9131462608a4612ab9906ed0dddea50": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2830a5e5bcb64d699ca0eb4d6ee52bca": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "40e82f9340574090a1bdb9181c6c073a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_87c71f9fe11b44d3bf515969199ca63e", "style": "IPY_MODEL_5e911bafcca34bcfb7d5085e81b17ba6", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_d36ed table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d36ed thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d36ed tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d36ed th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_d36ed td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_d36ed\">\n  <thead>\n    <tr>\n      <th id=\"T_d36ed_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_d36ed_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_d36ed_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_d36ed_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_d36ed_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_d36ed_row0_col2\" class=\"data row0 col2\" >Passed for 1 prediction properties out of 1 prediction properties.\nFound prediction property \"Samples Per Class\" has the highest categorical drift score: 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "87c71f9fe11b44d3bf515969199ca63e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e911bafcca34bcfb7d5085e81b17ba6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d1868200ee3340378e46678b869431bf": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b4e90750bff9487aa987f81a8fbc667d", "IPY_MODEL_056cb3a92c5f4dd9b930f3df2642ffa9", "IPY_MODEL_697919373ca74caaaa05d3823b70b92b", "IPY_MODEL_03330d36fa3b44b490713e04f10a37b5", "IPY_MODEL_5bd48d6fa12a480e9a39b809d3f8831b"], "layout": "IPY_MODEL_fe7aacb1785e48528707941f5e83ea40"}}, "b4e90750bff9487aa987f81a8fbc667d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8eb7cd029c6e4be5aed9832004808370", "style": "IPY_MODEL_b513500bd4e74d8290d7914d872cf318", "value": "<h5><b>Additional Outputs</b></h5>"}}, "8eb7cd029c6e4be5aed9832004808370": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b513500bd4e74d8290d7914d872cf318": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "056cb3a92c5f4dd9b930f3df2642ffa9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_276eac505613496a81411ccdf1a4988f", "style": "IPY_MODEL_f7114019f8ac4ef785359a217672244d", "value": "<div><span>The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following prediction properties: ['Samples Per Class']. </span></div>"}}, "276eac505613496a81411ccdf1a4988f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f7114019f8ac4ef785359a217672244d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "697919373ca74caaaa05d3823b70b92b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8037a897de844e53b86678555abe2566", "style": "IPY_MODEL_b387a47b38cc4524b94f4e59db36a63e", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "8037a897de844e53b86678555abe2566": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b387a47b38cc4524b94f4e59db36a63e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "03330d36fa3b44b490713e04f10a37b5": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.15.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.0], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "da1c9163-0a36-4bb1-81d8-d4e9e7789704"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": ["aedes_albopictus", "aedes_vexans", "anopheles_sinensis", "culex_pipiens", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [5]}, "yaxis": "y2", "type": "bar", "uid": "a329e786-7ffb-4536-8fa9-e7fb20588df9"}, {"marker": {"color": "#69b3a2"}, "name": 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"type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Samples Per Class", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 7], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "fixedrange": true, "rangemode": "normal", "title": {"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "f2D+nGjt0T8fdKvaGmbUP1Ey/VRAVMM/PsTCaPa2yD/QgLFLCTetPw=="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "U0oppZRS0j+VUkoppZTWP1NKKaWUUsI/+N577733xj+dc84555yjPw=="}]}, "5bd48d6fa12a480e9a39b809d3f8831b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d6b78877ae36413da431dc66796fad77", "style": 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"LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d13718259ccd4af587a9da8300a1e660": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "16e330b257d9493c88999bac563451d3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_36911bf33a3047a0a75e2e72d97380dd", "IPY_MODEL_59f51d51fc9446ffa0ec8d369079e001"], "layout": "IPY_MODEL_03acef33c9e34b188fb6f20c51b26ca1"}}, "36911bf33a3047a0a75e2e72d97380dd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_43537b84dafe444da1c1780865f7eb93", "style": "IPY_MODEL_ca4ddafeb64b415c96a12b8eb93df54b", "value": "<span id=\"summary_KF5B1SA3Y5T37SZPF9S9EHWHG-section-XHDCV\"></span>"}}, "43537b84dafe444da1c1780865f7eb93": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ca4ddafeb64b415c96a12b8eb93df54b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59f51d51fc9446ffa0ec8d369079e001": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Other"}, "children": ["IPY_MODEL_dc481eac3520473caa2b56f43d65a662"], "layout": "IPY_MODEL_9528465ffccb4d148f338535098c6c4b", "selected_index": null}}, "dc481eac3520473caa2b56f43d65a662": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_91d2d813c57d4898afdf5ba9966ec7e7", "IPY_MODEL_410019e0604e42828ce3367d0b901264", "IPY_MODEL_7742438f770946148f5fc05ca4ee01e1", "IPY_MODEL_9fcd7f3190614b2aa28b406b659f8a75", "IPY_MODEL_2dcf3e31bc2141b084201b91419d240a"], "layout": "IPY_MODEL_f9b0011ecab748989596281f09bf6eab"}}, "91d2d813c57d4898afdf5ba9966ec7e7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7eec2ebb6221499cbb6640a0a8fd5c7d", "style": "IPY_MODEL_13082ffdf8f44b598aa7e69dd6b8c27b", "value": "<style type=\"text/css\">\n#T_987f2 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_987f2 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_987f2 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_987f2 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_987f2 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_987f2\">\n  <thead>\n    <tr>\n      <th id=\"T_987f2_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_987f2_level0_col1\" class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_987f2_row0_col0\" class=\"data row0 col0\" ><a href=\"#ConfusionMatrix_KF5B1SA3Y5T37SZPF9S9EHWHG-section-XHDCV\">Confusion Matrix</a></td>\n      <td id=\"T_987f2_row0_col1\" class=\"data row0 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_987f2_row1_col0\" class=\"data row1 col0\" ><a href=\"#ConfusionMatrix_KF5B1SA3Y5T37SZPF9S9EHWHG-section-XHDCV\">Confusion Matrix</a></td>\n      <td id=\"T_987f2_row1_col1\" class=\"data row1 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7eec2ebb6221499cbb6640a0a8fd5c7d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "13082ffdf8f44b598aa7e69dd6b8c27b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "410019e0604e42828ce3367d0b901264": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ee1e86a92f9945a58f123cef0cd09ab3", "style": "IPY_MODEL_cae4379df0724d4696824117ad484192", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "ee1e86a92f9945a58f123cef0cd09ab3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cae4379df0724d4696824117ad484192": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7742438f770946148f5fc05ca4ee01e1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_d0c51482d5fb426abc4c0556be3bae16", "IPY_MODEL_2f68e42976814d4ba0653e21a152c397", "IPY_MODEL_f1d2c50077474b6b846fb05c3fd64108"], "layout": "IPY_MODEL_d8ede2e76dc748fe86defc12e73a21bf"}}, "d0c51482d5fb426abc4c0556be3bae16": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_582a86f8cabd4f8d9b7a9f282b9cd5bf", "style": "IPY_MODEL_f9ed6833f4de4688873af8cf94f43a18", "value": "<h4 id=\"ConfusionMatrix_KF5B1SA3Y5T37SZPF9S9EHWHG-section-XHDCV\"><b>Confusion Matrix</b></h4>"}}, "582a86f8cabd4f8d9b7a9f282b9cd5bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9ed6833f4de4688873af8cf94f43a18": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2f68e42976814d4ba0653e21a152c397": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0850403ac8e74022b00b3367ac47bb82", "style": "IPY_MODEL_70fccf9bbb784914b6176bf6f1bb7a44", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "0850403ac8e74022b00b3367ac47bb82": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "70fccf9bbb784914b6176bf6f1bb7a44": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f1d2c50077474b6b846fb05c3fd64108": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_787a718bab1c42ca989cf1ec3e87890a", "IPY_MODEL_886924faae56437b9ea234ee8a3a8c44", "IPY_MODEL_e6c39467fe4e47c0a594184ec34d32bf", "IPY_MODEL_ceb091de90094d828c49ec2afc91f2af", "IPY_MODEL_b5d35ae284574dae8c74f130c90a61a5"], "layout": "IPY_MODEL_0b400f37daab4b0ca229aef783e7dde9"}}, "787a718bab1c42ca989cf1ec3e87890a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_65fdde077c61453ab36541eba741ce84", "style": "IPY_MODEL_deeb68f855e34b22a87f41671a5f7c46", "value": "<h5><b>Additional Outputs</b></h5>"}}, "65fdde077c61453ab36541eba741ce84": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "deeb68f855e34b22a87f41671a5f7c46": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "886924faae56437b9ea234ee8a3a8c44": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5a69985cd92b48a3b336e1ed96dcac8b", "style": "IPY_MODEL_06c3fe0b17344dd9892eb2c00e1aea66", "value": "<div>Showing 10 of 5 classes:</div>"}}, "5a69985cd92b48a3b336e1ed96dcac8b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "06c3fe0b17344dd9892eb2c00e1aea66": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e6c39467fe4e47c0a594184ec34d32bf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c63a330c31e04feab287aad8a0893444", "style": "IPY_MODEL_3161d1100b5b485d8a03b31707030c55", "value": "<div>The overall accuracy of your model is: 72.82%.<br>Best accuracy achieved on samples with <b>aedes_albopictus</b> label (100.0%).<br>Worst accuracy achieved on samples with <b>culex_tritaeniorhynchus</b> label (22.09%).</div>"}}, "c63a330c31e04feab287aad8a0893444": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3161d1100b5b485d8a03b31707030c55": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ceb091de90094d828c49ec2afc91f2af": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.15.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[20.16, 100.0, 70.42], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [21.17, 100.0, 60.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[2.82, 14.0, 19.72], [3.83, 19.0, 26.76], [12.5, 62.0, 87.32], [1.01, 5.0, 7.04], [0.0, 0.0, 0.0]], [[1.01, 4.76, 5.62], [2.42, 11.43, 13.48], [0.81, 3.81, 4.49], [16.94, 80.0, 94.38], [0.0, 0.0, 0.0]], [[4.64, 26.74, 121.05], [7.86, 45.35, 205.26], [1.01, 5.81, 26.32], [0.0, 0.0, 0.0], [3.83, 22.09, 100.0]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[100.0, 0.0, 0.0, 0.0, 0.0], [0.0, 105.0, 0.0, 0.0, 0.0], [14.0, 19.0, 62.0, 5.0, 0.0], [5.0, 12.0, 4.0, 84.0, 0.0], [23.0, 39.0, 5.0, 0.0, 19.0]], "texttemplate": "%{text}", "x": ["aedes_albopictus", "aedes_vexans", "anopheles_sinensis", "culex_pipiens", "culex_tritaeniorhynchus"], "xgap": 1, "y": ["aedes_albopictus", "aedes_vexans", "anopheles_sinensis", "culex_pipiens", "culex_tritaeniorhynchus"], "ygap": 1, "z": [["100", "0", "0", "0", "0"], ["0", "100", "0", "0", "0"], ["14", "19", "62", "5", "0"], ["4.76", "11.43", "3.81", "80", "0"], ["26.74", "45.35", "5.81", "0", "22.09"]], "type": "heatmap", "uid": "bfe2e4a2-7f8f-4bd9-a1dd-f2501314dc17"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 600, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "b5d35ae284574dae8c74f130c90a61a5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_089f4fe075374620b7a1ed970dd73585", "style": "IPY_MODEL_e5792a931db64f25a174ebfd74417601", "value": "<br><a href=\"#summary_KF5B1SA3Y5T37SZPF9S9EHWHG-section-XHDCV\" style=\"font-size: 14px;\">Go to top</a>"}}, "089f4fe075374620b7a1ed970dd73585": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5792a931db64f25a174ebfd74417601": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0b400f37daab4b0ca229aef783e7dde9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", 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"@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_1ee4953c5f714e09a9c336ecb53bc4fb", "IPY_MODEL_4d6f5df95eb14c1797221f3724dfa937", "IPY_MODEL_59fddb1f35364027840c0ffc8a750b31"], "layout": "IPY_MODEL_63a6063602c145f180606f8a50c49a3d"}}, "1ee4953c5f714e09a9c336ecb53bc4fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4ea40e6990b74c77b8371cd17f59a4c3", "style": "IPY_MODEL_18e0a76908664dca943964d2d999a080", "value": "<h4 id=\"ConfusionMatrix_KF5B1SA3Y5T37SZPF9S9EHWHG-section-XHDCV\"><b>Confusion Matrix</b></h4>"}}, "4ea40e6990b74c77b8371cd17f59a4c3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "18e0a76908664dca943964d2d999a080": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4d6f5df95eb14c1797221f3724dfa937": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_50cef46404984953ae766623904c423e", "style": "IPY_MODEL_f6aaa925283e4f4c9549f30a846dc8c8", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.17/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "50cef46404984953ae766623904c423e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f6aaa925283e4f4c9549f30a846dc8c8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59fddb1f35364027840c0ffc8a750b31": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_7e729bc71b3b49a5ace34021292c2928", "IPY_MODEL_aa6a7b6c4605492892ed71cacd056a68", "IPY_MODEL_30f2522a48eb4e2a8df759690f6011dc", "IPY_MODEL_e0ac19889e48476fb076fb4f2ea13dc3", "IPY_MODEL_05e95351d7cb4a21949a5059e3d57d66"], "layout": "IPY_MODEL_62716eb9076a4a9d883fcb991741decc"}}, "7e729bc71b3b49a5ace34021292c2928": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eb0bdbd4be824ee8981d002aad122fca", "style": "IPY_MODEL_0272949368de4ec4bfdee4044bbb5c6b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "eb0bdbd4be824ee8981d002aad122fca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0272949368de4ec4bfdee4044bbb5c6b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "aa6a7b6c4605492892ed71cacd056a68": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_21d5149b29cc4edbb94120e10ec411a9", "style": "IPY_MODEL_410aedc19a8a486aaa7bfabbd45ae3c7", "value": "<div>Showing 10 of 5 classes:</div>"}}, "21d5149b29cc4edbb94120e10ec411a9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "410aedc19a8a486aaa7bfabbd45ae3c7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "30f2522a48eb4e2a8df759690f6011dc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4f2757e6c7fa452ab625b8c7faa1303a", "style": "IPY_MODEL_f02a10f609ce4ec188cf1656c5ee4145", "value": "<div>The overall accuracy of your model is: 78.99%.<br>Best accuracy achieved on samples with <b>aedes_albopictus</b> label (100.0%).<br>Worst accuracy achieved on samples with <b>culex_tritaeniorhynchus</b> label (27.05%).</div>"}}, "4f2757e6c7fa452ab625b8c7faa1303a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f02a10f609ce4ec188cf1656c5ee4145": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e0ac19889e48476fb076fb4f2ea13dc3": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.15.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[20.52, 100.0, 73.25], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [18.9, 100.0, 59.31], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[2.59, 13.12, 17.18], [1.96, 9.91, 12.98], [14.64, 74.05, 96.95], [0.58, 2.92, 3.82], [0.0, 0.0, 0.0]], [[0.46, 2.34, 2.39], [0.63, 3.22, 3.28], [0.12, 0.58, 0.6], [18.5, 93.86, 95.82], [0.0, 0.0, 0.0]], [[4.44, 21.04, 77.78], [10.37, 49.18, 181.82], [0.35, 1.64, 6.06], [0.23, 1.09, 4.04], [5.71, 27.05, 100.0]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[356.0, 0.0, 0.0, 0.0, 0.0], [0.0, 328.0, 0.0, 0.0, 0.0], [45.0, 34.0, 254.0, 10.0, 0.0], [8.0, 11.0, 2.0, 321.0, 0.0], [77.0, 180.0, 6.0, 4.0, 99.0]], "texttemplate": "%{text}", "x": ["aedes_albopictus", "aedes_vexans", "anopheles_sinensis", "culex_pipiens", "culex_tritaeniorhynchus"], "xgap": 1, "y": ["aedes_albopictus", "aedes_vexans", "anopheles_sinensis", "culex_pipiens", 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"ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "05e95351d7cb4a21949a5059e3d57d66": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8500acdd155e467db7e685b7ec9362f0", "style": "IPY_MODEL_e264e5823697486f90b5df98ffdb5ff4", "value": "<br><a href=\"#summary_KF5B1SA3Y5T37SZPF9S9EHWHG-section-XHDCV\" style=\"font-size: 14px;\">Go to top</a>"}}, "8500acdd155e467db7e685b7ec9362f0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e264e5823697486f90b5df98ffdb5ff4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "62716eb9076a4a9d883fcb991741decc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "63a6063602c145f180606f8a50c49a3d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9b0011ecab748989596281f09bf6eab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9528465ffccb4d148f338535098c6c4b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "03acef33c9e34b188fb6f20c51b26ca1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b9a43b602934f6fb7e882376395d0dd": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5cf0dbaf11f04ae2b1f2b9f33c571a48", "IPY_MODEL_a3d72c34476e4c399eef40d44306845e"], "layout": "IPY_MODEL_9485a938f07f4ee284c6df7f563ea7b4"}}, "5cf0dbaf11f04ae2b1f2b9f33c571a48": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6a21b5b1c94543959d16d951837f595a", "style": "IPY_MODEL_258ea5d8959a4e36994b1a8513582ab7"}}, "6a21b5b1c94543959d16d951837f595a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "258ea5d8959a4e36994b1a8513582ab7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a3d72c34476e4c399eef40d44306845e": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Didn't Run"}, "children": ["IPY_MODEL_96e63b58573e4042a088fa02246bd20b"], "layout": "IPY_MODEL_6df1c80a82aa4a3cb03c673f17a4cbfc", "selected_index": null}}, "96e63b58573e4042a088fa02246bd20b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb34a20c8ff047548d4eaea644871073", "style": "IPY_MODEL_b9382f45fa414fe185e195781bc69403", "value": "<style type=\"text/css\">\n#T_3bb97 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3bb97 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3bb97 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3bb97 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3bb97 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3bb97\">\n  <thead>\n    <tr>\n      <th id=\"T_3bb97_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_3bb97_level0_col1\" class=\"col_heading level0 col1\" >Reason</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3bb97_row0_col0\" class=\"data row0 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_3bb97_row0_col1\" class=\"data row0 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_3bb97_row1_col0\" class=\"data row1 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_3bb97_row1_col1\" class=\"data row1 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_3bb97_row2_col0\" class=\"data row2 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_3bb97_row2_col1\" class=\"data row2 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_3bb97_row3_col0\" class=\"data row3 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_3bb97_row3_col1\" class=\"data row3 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "fb34a20c8ff047548d4eaea644871073": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b9382f45fa414fe185e195781bc69403": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6df1c80a82aa4a3cb03c673f17a4cbfc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9485a938f07f4ee284c6df7f563ea7b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b27a065eaaa146e4bd8ebb224aba5b62": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e4653a8ca9044696a53c995c43041902": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}}}
+{"version_major": 2, "version_minor": 0, "state": {"6f006a468a4e4ac89f5fcb5144a46e93": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "Model Evaluation Suite"}, "children": ["IPY_MODEL_03d4765b9fde493c8556059c0ec4f676"], "layout": "IPY_MODEL_83315a2cf01f432cb9df0464c3830b31"}}, "03d4765b9fde493c8556059c0ec4f676": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_af113442f31d4ff3b21edbfce9a31b1e", "IPY_MODEL_e2b13eeff1894db08e1dd0897237b392", "IPY_MODEL_2ac6914ad338415486a1a4614c12a721", "IPY_MODEL_d78bb065a9c94cc1a96581abbd644904", "IPY_MODEL_9b378594c52341b78bc57f0dd42bb108"], "layout": "IPY_MODEL_49ac380e6d0b47cba2209ad9864b7f68"}}, "af113442f31d4ff3b21edbfce9a31b1e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3a54f23ea30740f8a0c32d30b8332f73", "style": "IPY_MODEL_645be2ae5bbc426a809d2ff3c1951e99", "value": "\n<h1 id=\"summary_3JCZUCQMRUNJDU0ISB5IQNF74\">Model Evaluation Suite</h1>\n<p>\n    The suite is composed of various checks such as: Mean Average Recall Report, Simple Model Comparison, Mean Average Precision Report, etc...<br>\n    Each check may contain conditions (which will result in pass <span style=\"color: green;display:inline-block\">\u2713</span> /\n    fail <span style=\"color: red;display:inline-block\">\u2716</span> / warning <span style=\"color: orange;font-weight:bold;display:inline-block\">!</span> / error <span style=\"color: firebrick;font-weight:bold;display:inline-block\">\u2048</span>) as well as\n    other outputs such as plots or tables.<br>\n    Suites, checks and conditions can all be modified. Read more about\n    <a href=https://docs.deepchecks.com/stable/general/usage/customizations/auto_examples/plot_create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n</p>\n\n"}}, "3a54f23ea30740f8a0c32d30b8332f73": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "645be2ae5bbc426a809d2ff3c1951e99": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e2b13eeff1894db08e1dd0897237b392": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ef3bb4d927c04f9b9e15fbec3ab66d34", "IPY_MODEL_85e42b6211af4cab85911a8ff2dfe09b"], "layout": "IPY_MODEL_8967fd648a5d4062ad614507d29dc789"}}, "ef3bb4d927c04f9b9e15fbec3ab66d34": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6618e7ca6df8495a814806b601878a9c", "style": "IPY_MODEL_1bd90f22ece94155978bac5457c8b69a", "value": "<span id=\"summary_3JCZUCQMRUNJDU0ISB5IQNF74-section-T60PL\"></span>"}}, "6618e7ca6df8495a814806b601878a9c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1bd90f22ece94155978bac5457c8b69a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "85e42b6211af4cab85911a8ff2dfe09b": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Didn't Pass"}, "children": ["IPY_MODEL_4d8a590857314ae180fd4b3913925a16"], "layout": "IPY_MODEL_9f02a7774de14b8490ddbe96757e993e", "selected_index": null}}, "4d8a590857314ae180fd4b3913925a16": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0dfd40c7cca2449cbb8616687e6b821e", "IPY_MODEL_986c39d38318495ea6a228ff1cf0fc30", "IPY_MODEL_160c9782f8e44021b6846e3f6f4032e3", "IPY_MODEL_57cbe1079aba4fbbaabd3e7e6a4792a2", "IPY_MODEL_dce42c308ba54d46839cca025771e8c0", "IPY_MODEL_57cbe1079aba4fbbaabd3e7e6a4792a2", "IPY_MODEL_990560f4a73a40bd8a6d417b59fc2dac"], "layout": "IPY_MODEL_415b2fbacfa7475693219ee61c2921f9"}}, "0dfd40c7cca2449cbb8616687e6b821e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ccb3e991e6cf468b9e65c3a5f6171fcb", "style": "IPY_MODEL_313dc719a8b54e2899eeba1ec260338b", "value": "<style type=\"text/css\">\n#T_a5b86 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a5b86 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a5b86 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a5b86 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a5b86 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a5b86\">\n  <thead>\n    <tr>\n      <th id=\"T_a5b86_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_a5b86_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_a5b86_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_a5b86_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a5b86_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_a5b86_row0_col1\" class=\"data row0 col1\" ><a href=\"#ClassPerformance_3JCZUCQMRUNJDU0ISB5IQNF74-section-T60PL\">Class Performance</a></td>\n      <td id=\"T_a5b86_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_a5b86_row0_col3\" class=\"data row0 col3\" >1 scores failed. Found max degradation of 13.45% for metric Precision and class aedes_aegypti.</td>\n    </tr>\n    <tr>\n      <td id=\"T_a5b86_row1_col0\" class=\"data row1 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_a5b86_row1_col1\" class=\"data row1 col1\" ><a href=\"#WeakSegmentsPerformance_3JCZUCQMRUNJDU0ISB5IQNF74-section-T60PL\">Weak Segments Performance</a></td>\n      <td id=\"T_a5b86_row1_col2\" class=\"data row1 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_a5b86_row1_col3\" class=\"data row1 col3\" >Found a segment with cross entropy of -3.514 in comparison to an average score of -0.598 in sampled data.</td>\n    </tr>\n    <tr>\n      <td id=\"T_a5b86_row2_col0\" class=\"data row2 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_a5b86_row2_col1\" class=\"data row2 col1\" ><a href=\"#WeakSegmentsPerformance_3JCZUCQMRUNJDU0ISB5IQNF74-section-T60PL\">Weak Segments Performance</a></td>\n      <td id=\"T_a5b86_row2_col2\" class=\"data row2 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_a5b86_row2_col3\" class=\"data row2 col3\" >Found a segment with cross entropy of -0.779 in comparison to an average score of -0.124 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ccb3e991e6cf468b9e65c3a5f6171fcb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "313dc719a8b54e2899eeba1ec260338b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "986c39d38318495ea6a228ff1cf0fc30": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8021bcf4d8cb43d48f4c30d2cb70644d", "style": "IPY_MODEL_5be39719be234a54841492fa808bb014", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "8021bcf4d8cb43d48f4c30d2cb70644d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5be39719be234a54841492fa808bb014": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "160c9782f8e44021b6846e3f6f4032e3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_67bb4a4afd4c4509a7864cab29bae893", "IPY_MODEL_77b40eaee6214c71ad64ba237b6a2c69", "IPY_MODEL_5d25e9e4815d4abc8d7cb11c628ae597", "IPY_MODEL_094ef9cb5d05450d9ac8e088b0e6aa21"], "layout": "IPY_MODEL_37b25ab22098493392895dd9e6faede3"}}, "67bb4a4afd4c4509a7864cab29bae893": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d2cf01461be9419f8b0d7fa2c9821ca9", "style": "IPY_MODEL_d8fffec6054a4bc09f691049529ce01d", "value": "<h4 id=\"ClassPerformance_3JCZUCQMRUNJDU0ISB5IQNF74-section-T60PL\"><b>Class Performance</b></h4>"}}, "d2cf01461be9419f8b0d7fa2c9821ca9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8fffec6054a4bc09f691049529ce01d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "77b40eaee6214c71ad64ba237b6a2c69": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e7c6fd90b3c14159a900505f5d88359a", "style": "IPY_MODEL_fb277096e71949d9a0062b6ef01fee5b", "value": "<p>Summarize given metrics on a dataset and model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_class_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e7c6fd90b3c14159a900505f5d88359a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fb277096e71949d9a0062b6ef01fee5b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5d25e9e4815d4abc8d7cb11c628ae597": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_99c3b376aeda4822a5782f5d73b8a956", "style": "IPY_MODEL_ee0636cc469a4fa4b26f4a9db7240d14", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_b775e table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b775e thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b775e tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b775e th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b775e td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b775e\">\n  <thead>\n    <tr>\n      <th id=\"T_b775e_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_b775e_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_b775e_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_b775e_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_b775e_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_b775e_row0_col2\" class=\"data row0 col2\" >1 scores failed. Found max degradation of 13.45% for metric Precision and class aedes_aegypti.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "99c3b376aeda4822a5782f5d73b8a956": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ee0636cc469a4fa4b26f4a9db7240d14": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "094ef9cb5d05450d9ac8e088b0e6aa21": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0365182ef1664c7bbbbb5fa6449d1562", "IPY_MODEL_7fc52b5c6fd046cb878ec77880daf1b8", "IPY_MODEL_da6462ef1c5f438f92001feee33dea62"], "layout": "IPY_MODEL_2b54f54656ed4f058e7902a2918fda3e"}}, "0365182ef1664c7bbbbb5fa6449d1562": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_648a1718f56b41a19673b02e378da0b1", "style": "IPY_MODEL_0a0ba63c856a40c98c8558adbe28998f", "value": "<h5><b>Additional Outputs</b></h5>"}}, "648a1718f56b41a19673b02e378da0b1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0a0ba63c856a40c98c8558adbe28998f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7fc52b5c6fd046cb878ec77880daf1b8": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Precision<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": true, "x": ["culex_quinquefasciatus", "aedes_aegypti", "aedes_albopictus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y", "type": "histogram", "uid": "1488a91f-607c-4fdf-8bba-4bd469f3f1a9"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": ["culex_quinquefasciatus", "aedes_albopictus", "aedes_aegypti"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "histogram", "uid": "41a14721-fc81-41c9-ba24-0871c4dbbafb"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Precision<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": true, "x": ["culex_quinquefasciatus", "aedes_albopictus", "aedes_aegypti"], "xaxis": "x", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y", "type": "histogram", "uid": "ae29f92d-8427-4893-819a-5921da6f7fec"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": false, "x": ["culex_quinquefasciatus", "aedes_aegypti", "aedes_albopictus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "histogram", "uid": "8f73f8e3-6754-4536-bebb-6324e575f1ef"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "Precision", "x": 0.2375, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "Recall", "x": 0.7625, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Dataset"}, "tracegroupgap": 0}, "margin": {"t": 60}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": 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"linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "xaxis": {"anchor": "y", "domain": [0.0, 0.475], "title": {"text": "Class"}, "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true, "title": {"text": "Value"}}, "xaxis2": {"anchor": "y2", "domain": [0.525, 1.0], "matches": "x", "title": {"text": "Class"}, "type": "category"}, "yaxis2": {"anchor": "x2", "domain": [0.0, 1.0], "showticklabels": true, "title": {"text": "Value"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "AAAAAAAA8D/MIj6BVHLuP7yVfQ6pCe4/"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "FDuxEzux7z/ZiZ3YiZ3uP3ZiJ3ZiJ+4/"}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "AAAAAAAA8D8AAAAAAADsP1paWlpaWuo/"}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "AAAAAAAA7j8AAAAAAADsPwAAAAAAAOw/"}]}, "da6462ef1c5f438f92001feee33dea62": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3440daa6d3204212845b8d87b606368f", "style": "IPY_MODEL_c8733db9ea5c4aaabff7004f029fe08d", "value": "<br><a href=\"#summary_3JCZUCQMRUNJDU0ISB5IQNF74-section-T60PL\" style=\"font-size: 14px;\">Go to top</a>"}}, "3440daa6d3204212845b8d87b606368f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c8733db9ea5c4aaabff7004f029fe08d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2b54f54656ed4f058e7902a2918fda3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37b25ab22098493392895dd9e6faede3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "57cbe1079aba4fbbaabd3e7e6a4792a2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ee3d363b41f2402bb66d8b559186cb2f", "style": "IPY_MODEL_efbf60e00dcc41a8942e28dae06a8e7f", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "ee3d363b41f2402bb66d8b559186cb2f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "efbf60e00dcc41a8942e28dae06a8e7f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dce42c308ba54d46839cca025771e8c0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_365f538dc3e2468bb28413b534145a6d", "IPY_MODEL_fab31f63c7774a53b1c90cd39a61642b", "IPY_MODEL_c80d39ff7a5f4507ae057d86f07a55a3", "IPY_MODEL_a0a36272ee394bbbb4baa0a1e193b37c"], "layout": "IPY_MODEL_c404cafd67de436d993772211c127344"}}, "365f538dc3e2468bb28413b534145a6d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5e30213d134647148cb37eed8ba4b470", "style": "IPY_MODEL_d2ddbd055eb0492da21a5d682d529709", "value": "<h4 id=\"WeakSegmentsPerformance_3JCZUCQMRUNJDU0ISB5IQNF74-section-T60PL\"><b>Weak Segments Performance</b></h4>"}}, "5e30213d134647148cb37eed8ba4b470": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d2ddbd055eb0492da21a5d682d529709": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fab31f63c7774a53b1c90cd39a61642b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9ab9920812d94515a3609d24e6be8c60", "style": "IPY_MODEL_7e0ea1c534794210bf1fdc0966cf72a7", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "9ab9920812d94515a3609d24e6be8c60": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7e0ea1c534794210bf1fdc0966cf72a7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c80d39ff7a5f4507ae057d86f07a55a3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ff39efd5a43d47679bbd38a013286484", "style": "IPY_MODEL_1165ed7518c94723bf998f8d29193624", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a22d4 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a22d4 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a22d4 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a22d4 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a22d4 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a22d4\">\n  <thead>\n    <tr>\n      <th id=\"T_a22d4_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_a22d4_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_a22d4_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a22d4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_a22d4_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_a22d4_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -3.514 in comparison to an average score of -0.598 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ff39efd5a43d47679bbd38a013286484": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1165ed7518c94723bf998f8d29193624": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a0a36272ee394bbbb4baa0a1e193b37c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4702dc1d65ea41ab939b2971efe08aa7", "IPY_MODEL_f1181f27b95c4271a79d523bcadcc561", "IPY_MODEL_dac733985f564ac19c2fa1dc21ee4c7a", "IPY_MODEL_315b02aee69548fb916b9c50dc6b7f51"], "layout": "IPY_MODEL_d40036a8f85b47669fb22ca624b4af92"}}, "4702dc1d65ea41ab939b2971efe08aa7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1a92d90ae3394897861fca64d016dbf7", "style": "IPY_MODEL_ec2acf909f0147ad82541b4e24ac486c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1a92d90ae3394897861fca64d016dbf7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ec2acf909f0147ad82541b4e24ac486c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f1181f27b95c4271a79d523bcadcc561": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_447510121e7946af8fc776d719a865c4", "style": "IPY_MODEL_2ecd7b5f2c824b90ad2744939d63aec3", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "447510121e7946af8fc776d719a865c4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2ecd7b5f2c824b90ad2744939d63aec3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dac733985f564ac19c2fa1dc21ee4c7a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], 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{"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Cross Entropy (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "RMS Contrast"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false, "title": {"text": "Mean Red Relative Intensity"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "c9a9601c68ba4d549b73940ff1b39f98": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_41a95927cab2434ea55c34384916b3d9", "style": "IPY_MODEL_88237f4d1a774a07b8acfca2533576db", "value": "<div>Check ran on 48 data samples. Cross Entropy on the full data set is -0.6.</div>"}}, "41a95927cab2434ea55c34384916b3d9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "88237f4d1a774a07b8acfca2533576db": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f8ff076ff13441c0a25b3aa3ba54db64": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2190f7f0036c4f81afe36f5fa7959236": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5f505d38b2d74127a35975f2182f53a3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "315b02aee69548fb916b9c50dc6b7f51": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_05f514c2bd7241bc8fe75447992daf92", "style": "IPY_MODEL_24bd9a72569b4e699cf6658988a2d925", "value": "<br><a href=\"#summary_3JCZUCQMRUNJDU0ISB5IQNF74-section-T60PL\" style=\"font-size: 14px;\">Go to top</a>"}}, "05f514c2bd7241bc8fe75447992daf92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "24bd9a72569b4e699cf6658988a2d925": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d40036a8f85b47669fb22ca624b4af92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c404cafd67de436d993772211c127344": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "990560f4a73a40bd8a6d417b59fc2dac": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_b357bd395b264276927e97b64bf3b4aa", "IPY_MODEL_85d2b58cd2e94cb6aa75f160c2ba0754", "IPY_MODEL_ff513d1b749447e0945fb72941d91c3c", "IPY_MODEL_ea59da0b54684440855cae8a2da5062c"], "layout": "IPY_MODEL_252984a4abbd4840996df526204352df"}}, "b357bd395b264276927e97b64bf3b4aa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ad3e3c6fae2d4450a9107698596bd77b", "style": "IPY_MODEL_8b817ac875674d5d899946a64306519a", "value": "<h4 id=\"WeakSegmentsPerformance_3JCZUCQMRUNJDU0ISB5IQNF74-section-T60PL\"><b>Weak Segments Performance</b></h4>"}}, "ad3e3c6fae2d4450a9107698596bd77b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8b817ac875674d5d899946a64306519a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "85d2b58cd2e94cb6aa75f160c2ba0754": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2531eec12cfe432db7593e04da92eac6", "style": "IPY_MODEL_2dfb79b3d91141baaab47d2f3401f14e", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "2531eec12cfe432db7593e04da92eac6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2dfb79b3d91141baaab47d2f3401f14e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ff513d1b749447e0945fb72941d91c3c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ef6cfc61c8d9464eb1fce0fe082bce59", "style": "IPY_MODEL_b76cba0eda3444a984fe6d482b231d48", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_b1422 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b1422 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b1422 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b1422 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b1422 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b1422\">\n  <thead>\n    <tr>\n      <th id=\"T_b1422_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_b1422_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_b1422_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_b1422_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_b1422_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_b1422_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -0.779 in comparison to an average score of -0.124 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ef6cfc61c8d9464eb1fce0fe082bce59": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b76cba0eda3444a984fe6d482b231d48": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ea59da0b54684440855cae8a2da5062c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ca30e8b1c5a646f7b3cec1acd14712e9", "IPY_MODEL_7d911e4433af48479964fcb7bb417da5", "IPY_MODEL_9a20fa136a6342bc8f5a34e59e9de0da", "IPY_MODEL_c5c5ff4ffbcc40bc975297b7c8e81b76"], "layout": "IPY_MODEL_0f2e913b87ce4001bd9b0b1684beeb63"}}, "ca30e8b1c5a646f7b3cec1acd14712e9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_53a44db4e3464f1baa05b438cd48aad1", "style": "IPY_MODEL_6f22cd5d8c1f406ab730c51d40593f92", "value": "<h5><b>Additional Outputs</b></h5>"}}, "53a44db4e3464f1baa05b438cd48aad1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6f22cd5d8c1f406ab730c51d40593f92": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7d911e4433af48479964fcb7bb417da5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1422ce8a9d474ce5af94988a3fe64af9", "style": "IPY_MODEL_30e5f520ddd247beb6611f033dc05980", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "1422ce8a9d474ce5af94988a3fe64af9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "30e5f520ddd247beb6611f033dc05980": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9a20fa136a6342bc8f5a34e59e9de0da": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b7ed2cdb80d64607a011f72509863005", "IPY_MODEL_5cb1d04fc6c54921825ca5a1db375b65"], "layout": "IPY_MODEL_2da278f8a23c42fc8da6b707f527208d"}}, "b7ed2cdb80d64607a011f72509863005": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_603e21f7d3c244e8895e064adfb50425", "style": "IPY_MODEL_1e0446387e7a4961b479690c395aaa95", "value": "<style>.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {min-width: fit-content;}</style>"}}, "603e21f7d3c244e8895e064adfb50425": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1e0446387e7a4961b479690c395aaa95": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5cb1d04fc6c54921825ca5a1db375b65": {"model_name": "TabModel", "model_module": "@jupyter-widgets/controls", 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"children": ["IPY_MODEL_d89cf12c5689423bb83e1f67d5201bfe", "IPY_MODEL_a59d399a783f45509cf8e54ae07e092a", "IPY_MODEL_b4a820cdb085473688423a0385d45576", "IPY_MODEL_77f2a84313d9429281ca6551d9bf3990", "IPY_MODEL_2015f19f29194b19a6195405fb250df5"], "layout": "IPY_MODEL_26dba1d0391045959a834995f6adfd92"}}, "d89cf12c5689423bb83e1f67d5201bfe": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_54164581ae4845f78cb6c237e26c8c72", "style": "IPY_MODEL_45858a82c7ca4e5e99ab73f917f1a6df", "value": "<style type=\"text/css\">\n#T_89814 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_89814 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_89814 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_89814 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_89814 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_89814\">\n  <thead>\n    <tr>\n      <th id=\"T_89814_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_89814_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_89814_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_89814_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_89814_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_89814_row0_col1\" class=\"data row0 col1\" ><a href=\"#PredictionDrift_3JCZUCQMRUNJDU0ISB5IQNF74-section-OZJCN\">Prediction Drift</a></td>\n      <td id=\"T_89814_row0_col2\" class=\"data row0 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_89814_row0_col3\" class=\"data row0 col3\" >Passed for 1 prediction properties out of 1 prediction properties.\nFound prediction property \"Samples Per Class\" has the highest categorical drift score: 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_89814_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_89814_row1_col1\" class=\"data row1 col1\" ><a href=\"#SimpleModelComparison_3JCZUCQMRUNJDU0ISB5IQNF74-section-OZJCN\">Simple Model Comparison</a></td>\n      <td id=\"T_89814_row1_col2\" class=\"data row1 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_89814_row1_col3\" class=\"data row1 col3\" >Found minimal gain of 69.7% for metric F1 and class aedes_aegypti</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "54164581ae4845f78cb6c237e26c8c72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "45858a82c7ca4e5e99ab73f917f1a6df": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a59d399a783f45509cf8e54ae07e092a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5dbc9ae09c7d4cc1ac9003b631104b05", "style": "IPY_MODEL_bf470a91b2c34e92b4bbf5b069790829", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "5dbc9ae09c7d4cc1ac9003b631104b05": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bf470a91b2c34e92b4bbf5b069790829": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b4a820cdb085473688423a0385d45576": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_46fcd0a97049421dab94e78bc89ff13c", "IPY_MODEL_3c036bf038ef4426801539ebb8bf8473", "IPY_MODEL_2228e350bbe44c5e8b944d42e719a6cc", "IPY_MODEL_9fd051c8a8fe46c0a2f78707f65aece1"], "layout": "IPY_MODEL_1e94aa741e2342fdba3c52eff8445f16"}}, "46fcd0a97049421dab94e78bc89ff13c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_77f805ee370a4acba4a3fc486c44d820", "style": "IPY_MODEL_7f7628662955492e82b9d007f47ec672", "value": "<h4 id=\"PredictionDrift_3JCZUCQMRUNJDU0ISB5IQNF74-section-OZJCN\"><b>Prediction Drift</b></h4>"}}, "77f805ee370a4acba4a3fc486c44d820": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7f7628662955492e82b9d007f47ec672": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3c036bf038ef4426801539ebb8bf8473": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4426a3ae34734eae969b4a050b21a4a2", "style": "IPY_MODEL_ae8cc6e1280c4727a2632a21efa5bd9f", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "4426a3ae34734eae969b4a050b21a4a2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae8cc6e1280c4727a2632a21efa5bd9f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2228e350bbe44c5e8b944d42e719a6cc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a4d77fd4115d4ff5abf724256a534729", "style": "IPY_MODEL_aae2d66eb53540dd9e7abf2421e71893", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_b2669 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b2669 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b2669 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b2669 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_b2669 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_b2669\">\n  <thead>\n    <tr>\n      <th id=\"T_b2669_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_b2669_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_b2669_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_b2669_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_b2669_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_b2669_row0_col2\" class=\"data row0 col2\" >Passed for 1 prediction properties out of 1 prediction properties.\nFound prediction property \"Samples Per Class\" has the highest categorical drift score: 0</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a4d77fd4115d4ff5abf724256a534729": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aae2d66eb53540dd9e7abf2421e71893": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9fd051c8a8fe46c0a2f78707f65aece1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_39d22d6bffde47af8ab86e156e17c149", "IPY_MODEL_02e70e82214945129974f8b8d700ce7f", "IPY_MODEL_4b5a0dde4bb444eaa3dd8d244493fa53", "IPY_MODEL_4f33b55dc01d453eaab858733d121f1f", "IPY_MODEL_a1c5fbfcf14f48979addae2142ae7a66"], "layout": "IPY_MODEL_2a99186b95e644539d88cc3d5ad024ab"}}, "39d22d6bffde47af8ab86e156e17c149": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_627987c0ba6e452c83a37aae45934f88", "style": "IPY_MODEL_4694d450fec84de59f1b6bc31e1306c0", "value": "<h5><b>Additional Outputs</b></h5>"}}, "627987c0ba6e452c83a37aae45934f88": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4694d450fec84de59f1b6bc31e1306c0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "02e70e82214945129974f8b8d700ce7f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5a90b5757b974163a831ab849edd30bb", "style": "IPY_MODEL_8d066db8cee94dec9c5049c2083d1361", "value": "<div><span>The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following prediction properties: ['Samples Per Class']. </span></div>"}}, "5a90b5757b974163a831ab849edd30bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8d066db8cee94dec9c5049c2083d1361": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4b5a0dde4bb444eaa3dd8d244493fa53": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_44ced97f141d4d7e8f666e3e9542f9fe", "style": "IPY_MODEL_740df6d998c04349b4dccbcacdef9ec2", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "44ced97f141d4d7e8f666e3e9542f9fe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "740df6d998c04349b4dccbcacdef9ec2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4f33b55dc01d453eaab858733d121f1f": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.0], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "2cc52266-9723-483a-bf1d-db7d71d75500"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "bar", "uid": "9345a5ed-3ff9-476e-be26-bd6c34872d77"}, {"marker": {"color": "#69b3a2"}, "name": "Test Dataset", "x": ["aedes_aegypti", 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"x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "fixedrange": true, "rangemode": "normal", "title": {"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "DdIgDdIg1T/mW77lW77VPw3SIA3SINU/"}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "q6qqqqqq1j9VVVVVVVXVPwAAAAAAANQ/"}]}, "a1c5fbfcf14f48979addae2142ae7a66": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f1f5bc16dae2468bb3b22fc1822152b2", "style": "IPY_MODEL_bc85564ae3dc4c11aa5fda8c88e90d35", "value": "<br><a href=\"#summary_3JCZUCQMRUNJDU0ISB5IQNF74-section-OZJCN\" style=\"font-size: 14px;\">Go to top</a>"}}, "f1f5bc16dae2468bb3b22fc1822152b2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc85564ae3dc4c11aa5fda8c88e90d35": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2a99186b95e644539d88cc3d5ad024ab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1e94aa741e2342fdba3c52eff8445f16": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "77f2a84313d9429281ca6551d9bf3990": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6dd1b0b186614ef494776a93cf81fd70", "style": "IPY_MODEL_85fbbbca1ca2407593c32ac483f92f0d", "value": "<hr style=\"background-color: #eee;border: 0 none;color: 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"state": {"_dom_classes": [], "layout": "IPY_MODEL_d7fed46059664885b121e3b6f51c7a23", "style": "IPY_MODEL_f33ce498a5d44a769373ba7b62100174", "value": "<h4 id=\"SimpleModelComparison_3JCZUCQMRUNJDU0ISB5IQNF74-section-OZJCN\"><b>Simple Model Comparison</b></h4>"}}, "d7fed46059664885b121e3b6f51c7a23": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f33ce498a5d44a769373ba7b62100174": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "78c262eb5af745b798785c0b0e606546": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6914e0555f8341428ae22f2ce1bc8d42", "style": "IPY_MODEL_01037ae8b8b0415db55607bc15bc5468", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "6914e0555f8341428ae22f2ce1bc8d42": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "01037ae8b8b0415db55607bc15bc5468": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "56004652bec044e19c5e3d1641374fad": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_53842d2e088746b2809f503540fbb65b", "style": "IPY_MODEL_4f3719c83be44f96b892287a5ab38835", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_79da5 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_79da5 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_79da5 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_79da5 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_79da5 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_79da5\">\n  <thead>\n    <tr>\n      <th id=\"T_79da5_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_79da5_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_79da5_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_79da5_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_79da5_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_79da5_row0_col2\" class=\"data row0 col2\" >Found minimal gain of 69.7% for metric F1 and class aedes_aegypti</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "53842d2e088746b2809f503540fbb65b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4f3719c83be44f96b892287a5ab38835": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dc090967c3944d81bccf1cdfc3f7a4ad": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0f72e06237be44238131ebebdb0cf127", "IPY_MODEL_1568322786134d6c9a15c203c3147e16", "IPY_MODEL_38cf7fe3f2374696a99e77509ad59dc4"], "layout": "IPY_MODEL_02407ee04e47456087fe8d8fe90b15ec"}}, "0f72e06237be44238131ebebdb0cf127": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b2eebdf724c44b09b0cb271d3f5046b0", "style": "IPY_MODEL_32f9432f68ef41c39f068682b5a610b2", "value": "<h5><b>Additional Outputs</b></h5>"}}, "b2eebdf724c44b09b0cb271d3f5046b0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "32f9432f68ef41c39f068682b5a610b2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1568322786134d6c9a15c203c3147e16": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Simple Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Simple Model", "marker": {"color": "#2191FB", "pattern": {"shape": ""}}, "name": "Simple Model", "offsetgroup": "Simple Model", "orientation": "v", "showlegend": true, "x": 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"@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ec1a7620ffa74f8b9f8e325c2e5f6b7f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "02407ee04e47456087fe8d8fe90b15ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "28881f17939d4622b7acd3df230fd02c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "26dba1d0391045959a834995f6adfd92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3f5989b30eae4691b504d4569e0bc808": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "92a55372c1964c14871ff179b15dc0cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d78bb065a9c94cc1a96581abbd644904": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0e8f3679f6c84226ace108c29305b0d7", "IPY_MODEL_33a9263e3547478b8c14099b1838b9d4"], "layout": "IPY_MODEL_ff490823794743d09a3fc7678689f9ad"}}, "0e8f3679f6c84226ace108c29305b0d7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_464d9ec3f69e4df2bbaa828c76ff65d7", "style": "IPY_MODEL_6d57288c150e4ca792f812025d7de701", "value": "<span id=\"summary_3JCZUCQMRUNJDU0ISB5IQNF74-section-S6G29\"></span>"}}, "464d9ec3f69e4df2bbaa828c76ff65d7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d57288c150e4ca792f812025d7de701": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "33a9263e3547478b8c14099b1838b9d4": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Other"}, "children": ["IPY_MODEL_970f2d59061048e4ad35c70918b1573c"], "layout": "IPY_MODEL_241fc04a33994853a7402e60171f91a9", "selected_index": null}}, "970f2d59061048e4ad35c70918b1573c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_934e7c7885524516946ca89e1a20bbbe", "IPY_MODEL_94b17071d01940568dd6cd8748101f26", "IPY_MODEL_0d9b5091259a4098a4fccc1986fdae2f", "IPY_MODEL_4196ac042c304bc3bfefe8c2134baa0a", "IPY_MODEL_1b7354bda0044efdb5cca74c6070278b"], "layout": "IPY_MODEL_c805c601a5c54b539efb568d3b40a35e"}}, "934e7c7885524516946ca89e1a20bbbe": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c7e3fc2c94f547a680ab68bfb89018b7", "style": "IPY_MODEL_27ad1e5aede04d4dab68b973bed69af3", "value": "<style type=\"text/css\">\n#T_c938d table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c938d thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c938d tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c938d th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c938d td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c938d\">\n  <thead>\n    <tr>\n      <th id=\"T_c938d_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_c938d_level0_col1\" class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c938d_row0_col0\" class=\"data row0 col0\" ><a href=\"#ConfusionMatrix_3JCZUCQMRUNJDU0ISB5IQNF74-section-S6G29\">Confusion Matrix</a></td>\n      <td id=\"T_c938d_row0_col1\" class=\"data row0 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_c938d_row1_col0\" class=\"data row1 col0\" ><a href=\"#ConfusionMatrix_3JCZUCQMRUNJDU0ISB5IQNF74-section-S6G29\">Confusion Matrix</a></td>\n      <td id=\"T_c938d_row1_col1\" class=\"data row1 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c7e3fc2c94f547a680ab68bfb89018b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "27ad1e5aede04d4dab68b973bed69af3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "94b17071d01940568dd6cd8748101f26": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_65dbfc9f1d444e329de95f44cead3f35", "style": "IPY_MODEL_c537706321cc43aeb84935ef810b3606", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "65dbfc9f1d444e329de95f44cead3f35": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c537706321cc43aeb84935ef810b3606": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0d9b5091259a4098a4fccc1986fdae2f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_32334ae21b524936936a38c0a250bc29", "IPY_MODEL_f201886f9e9d44d1bcc4e19783407963", "IPY_MODEL_472a3e47354d4640b9cd300b51aecc87"], "layout": "IPY_MODEL_c2750260934d4a908830b84715b82915"}}, "32334ae21b524936936a38c0a250bc29": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6562f4a1f9574d379e06b30ce06e3ba0", "style": "IPY_MODEL_44d48e8bbf4b428bb32b9bf2aeee77f1", "value": "<h4 id=\"ConfusionMatrix_3JCZUCQMRUNJDU0ISB5IQNF74-section-S6G29\"><b>Confusion Matrix</b></h4>"}}, "6562f4a1f9574d379e06b30ce06e3ba0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "44d48e8bbf4b428bb32b9bf2aeee77f1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f201886f9e9d44d1bcc4e19783407963": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_126263fe9c1d4e4e8383ec07826e1c8f", "style": "IPY_MODEL_076ac87b1eb44ed1a7a862ee3ef14df4", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "126263fe9c1d4e4e8383ec07826e1c8f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "076ac87b1eb44ed1a7a862ee3ef14df4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "472a3e47354d4640b9cd300b51aecc87": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ac7e66db7ec14c62a8dfdabd0f221620", "IPY_MODEL_721cfe3e409d47ec8c38968bbcd2005a", "IPY_MODEL_4449964d1d9d46678a86e4efa7a4bd10", "IPY_MODEL_4bee5479baca439a98899c3b2dfe60be", "IPY_MODEL_8c6153e80c8f474f9f7f3e42d26c2915"], "layout": "IPY_MODEL_541a9f7515b247a18985f8d03b14bc84"}}, "ac7e66db7ec14c62a8dfdabd0f221620": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_53a0955af7874895ae0c1a93b35d58c8", "style": "IPY_MODEL_2b9170ccb2234c7fb4592e41ee5d7dec", "value": "<h5><b>Additional Outputs</b></h5>"}}, "53a0955af7874895ae0c1a93b35d58c8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2b9170ccb2234c7fb4592e41ee5d7dec": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "721cfe3e409d47ec8c38968bbcd2005a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0eeb8e0afec74b338bbe1dbb840f2473", "style": "IPY_MODEL_3c5d8bfe564943f5add98f56f7e5e4ad", "value": "<div>Showing 10 of 3 classes:</div>"}}, "0eeb8e0afec74b338bbe1dbb840f2473": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c5d8bfe564943f5add98f56f7e5e4ad": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4449964d1d9d46678a86e4efa7a4bd10": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e1d04022a88d408bb5c28b7c1cac519f", "style": "IPY_MODEL_53a508dea4bd44158f5b1fbdbbd5c5c3", "value": "<div>The overall accuracy of your model is: 89.58%.<br>Best accuracy achieved on samples with <b>culex_quinquefasciatus</b> label (93.75%).<br>Worst accuracy achieved on samples with <b>aedes_aegypti</b> label (87.5%).</div>"}}, "e1d04022a88d408bb5c28b7c1cac519f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "53a508dea4bd44158f5b1fbdbbd5c5c3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4bee5479baca439a98899c3b2dfe60be": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[29.17, 87.5, 82.35], [4.17, 12.5, 11.76], [0.0, 0.0, 0.0]], [[4.17, 12.5, 12.5], [29.17, 87.5, 87.5], [0.0, 0.0, 0.0]], [[2.08, 6.25, 6.67], [0.0, 0.0, 0.0], [31.25, 93.75, 100.0]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[14.0, 2.0, 0.0], [2.0, 14.0, 0.0], [1.0, 0.0, 15.0]], "texttemplate": "%{text}", "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xgap": 1, "y": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "ygap": 1, "z": [["87.5", "12.5", "0"], ["12.5", "87.5", "0"], ["6.25", "0", "93.75"]], "type": "heatmap", "uid": "a0ecd184-0935-443a-b810-383280f6d5b1"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 600, "template": {"data": 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{"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "8c6153e80c8f474f9f7f3e42d26c2915": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dd935eca73d8489eb5b6a0faf0c76d4c", "style": "IPY_MODEL_bc308373ed8345108004004b51c4dd70", "value": "<br><a href=\"#summary_3JCZUCQMRUNJDU0ISB5IQNF74-section-S6G29\" style=\"font-size: 14px;\">Go to top</a>"}}, "dd935eca73d8489eb5b6a0faf0c76d4c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc308373ed8345108004004b51c4dd70": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "541a9f7515b247a18985f8d03b14bc84": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c2750260934d4a908830b84715b82915": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4196ac042c304bc3bfefe8c2134baa0a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_64674e80008c41c2b39bcda0bea100cd", "style": "IPY_MODEL_f5a05c89e4d847d3ad5f286af2339298", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "64674e80008c41c2b39bcda0bea100cd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f5a05c89e4d847d3ad5f286af2339298": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1b7354bda0044efdb5cca74c6070278b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_678c6af3e19a476c9ce23520c1a8fbc3", "IPY_MODEL_969720049289491987b6cd293a019551", "IPY_MODEL_af8a016605c848c09c7b985f365ad540"], "layout": "IPY_MODEL_22f0cf70666c494693049e362bd316eb"}}, "678c6af3e19a476c9ce23520c1a8fbc3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_022368d311c94da89845bdf68b6e1243", "style": "IPY_MODEL_759a47616a0d424bb667cf03e93bb45e", "value": "<h4 id=\"ConfusionMatrix_3JCZUCQMRUNJDU0ISB5IQNF74-section-S6G29\"><b>Confusion Matrix</b></h4>"}}, "022368d311c94da89845bdf68b6e1243": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "759a47616a0d424bb667cf03e93bb45e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "969720049289491987b6cd293a019551": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1023fe184e4a42f3aad3abf6c39589c0", "style": "IPY_MODEL_f74aced6e0e645d08b02682e229f96d5", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "1023fe184e4a42f3aad3abf6c39589c0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f74aced6e0e645d08b02682e229f96d5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "af8a016605c848c09c7b985f365ad540": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_cb2cc4b134ce4b88b48e0a19eb506376", "IPY_MODEL_207237cc1ae64b31b36614a1ae9309a9", "IPY_MODEL_ae8aba72c1a04cad8bf7777911cabdb4", "IPY_MODEL_bd8861ac284d4311a3ef069967d1e9c1", "IPY_MODEL_aaee30bfbf734d8aa08775f572505cbd"], "layout": "IPY_MODEL_5848e24db2df439ba6d51360f1ce1216"}}, "cb2cc4b134ce4b88b48e0a19eb506376": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4a4c620b4fee446ab4821009abc3173c", "style": "IPY_MODEL_fc3977841bc246d38fab3d505b41fe26", "value": "<h5><b>Additional Outputs</b></h5>"}}, "4a4c620b4fee446ab4821009abc3173c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc3977841bc246d38fab3d505b41fe26": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "207237cc1ae64b31b36614a1ae9309a9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5251371d4bee4c42995c91d2203b5afa", "style": "IPY_MODEL_5ac95a3ec9b24471bc362408a81e4ec6", "value": "<div>Showing 10 of 3 classes:</div>"}}, "5251371d4bee4c42995c91d2203b5afa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ac95a3ec9b24471bc362408a81e4ec6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ae8aba72c1a04cad8bf7777911cabdb4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e23e34f1f8694e78abe3178f6a11fb43", "style": "IPY_MODEL_090f521eb7364248a81135e24af5c7fd", "value": "<div>The overall accuracy of your model is: 96.31%.<br>Best accuracy achieved on samples with <b>culex_quinquefasciatus</b> label (99.04%).<br>Worst accuracy achieved on samples with <b>aedes_aegypti</b> label (94.23%).</div>"}}, "e23e34f1f8694e78abe3178f6a11fb43": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "090f521eb7364248a81135e24af5c7fd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bd8861ac284d4311a3ef069967d1e9c1": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[31.41, 94.23, 95.15], [1.92, 5.77, 5.83], [0.0, 0.0, 0.0]], [[1.44, 4.33, 4.25], [31.89, 95.67, 93.87], [0.0, 0.0, 0.0]], [[0.16, 0.48, 0.49], [0.16, 0.48, 0.49], 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