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README.md

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+---
+license: apache-2.0
+library_name: sklearn
+tags:
+- tabular-classification
+- baseline-trainer
+---
+
+## Baseline Model trained on diabetespmxrsn1x to apply classification on diabetes
+
+**Metrics of the best model:**
+
+accuracy             0.871795
+
+average_precision    0.518856
+
+roc_auc              0.883333
+
+recall_macro         0.883333
+
+f1_macro             0.801996
+
+Name: DecisionTreeClassifier(class_weight='balanced', max_depth=1), dtype: float64
+
+
+
+**See model plot below:**
+
+<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-4" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=                 continuous  dirty_float  ...  free_string  useless
+cholesterol            True        False  ...        False    False
+glucose                True        False  ...        False    False
+hdl_chol               True        False  ...        False    False
+chol_hdl_ratio        False        False  ...         True    False
+age                    True        False  ...        False    False
+gender                False        False  ...        False    False
+height                False        False  ...        False    False
+weight                 True        False  ...        False    False
+bmi                   False        False  ...         True    False
+systolic_bp            True        False  ...        False    False
+diastolic_bp           True        False  ...        False    False
+waist                 False        False  ...        False    False
+hip                   False        False  ...        False    False
+waist_hip_ratio       False        False  ...        False    False[14 rows x 7 columns])),(&#x27;decisiontreeclassifier&#x27;,DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, max_depth=1))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-10" type="checkbox" ><label for="sk-estimator-id-10" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;easypreprocessor&#x27;,EasyPreprocessor(types=                 continuous  dirty_float  ...  free_string  useless
+cholesterol            True        False  ...        False    False
+glucose                True        False  ...        False    False
+hdl_chol               True        False  ...        False    False
+chol_hdl_ratio        False        False  ...         True    False
+age                    True        False  ...        False    False
+gender                False        False  ...        False    False
+height                False        False  ...        False    False
+weight                 True        False  ...        False    False
+bmi                   False        False  ...         True    False
+systolic_bp            True        False  ...        False    False
+diastolic_bp           True        False  ...        False    False
+waist                 False        False  ...        False    False
+hip                   False        False  ...        False    False
+waist_hip_ratio       False        False  ...        False    False[14 rows x 7 columns])),(&#x27;decisiontreeclassifier&#x27;,DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, max_depth=1))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types=                 continuous  dirty_float  ...  free_string  useless
+cholesterol            True        False  ...        False    False
+glucose                True        False  ...        False    False
+hdl_chol               True        False  ...        False    False
+chol_hdl_ratio        False        False  ...         True    False
+age                    True        False  ...        False    False
+gender                False        False  ...        False    False
+height                False        False  ...        False    False
+weight                 True        False  ...        False    False
+bmi                   False        False  ...         True    False
+systolic_bp            True        False  ...        False    False
+diastolic_bp           True        False  ...        False    False
+waist                 False        False  ...        False    False
+hip                   False        False  ...        False    False
+waist_hip_ratio       False        False  ...        False    False[14 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-12" type="checkbox" ><label for="sk-estimator-id-12" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, max_depth=1)</pre></div></div></div></div></div></div></div>
+
+**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
+
+**Logs of training** including the models tried in the process can be found in logs.txt

clf.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0aa3c1b55abe3f70c7847e682626865932514bc2bf518fb0ce83a8258d26d1ce
+size 10960

logs.txt

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+Logging training
+Running DummyClassifier()
+accuracy: 0.846 average_precision: 0.154 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.458
+=== new best DummyClassifier() (using recall_macro):
+accuracy: 0.846 average_precision: 0.154 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.458
+
+Running GaussianNB()
+accuracy: 0.469 average_precision: 0.171 roc_auc: 0.646 recall_macro: 0.550 f1_macro: 0.426
+=== new best GaussianNB() (using recall_macro):
+accuracy: 0.469 average_precision: 0.171 roc_auc: 0.646 recall_macro: 0.550 f1_macro: 0.426
+
+Running MultinomialNB()
+accuracy: 0.826 average_precision: 0.295 roc_auc: 0.680 recall_macro: 0.542 f1_macro: 0.547
+Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
+accuracy: 0.872 average_precision: 0.519 roc_auc: 0.883 recall_macro: 0.883 f1_macro: 0.802
+=== new best DecisionTreeClassifier(class_weight='balanced', max_depth=1) (using recall_macro):
+accuracy: 0.872 average_precision: 0.519 roc_auc: 0.883 recall_macro: 0.883 f1_macro: 0.802
+
+Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
+accuracy: 0.885 average_precision: 0.552 roc_auc: 0.822 recall_macro: 0.816 f1_macro: 0.786
+Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
+accuracy: 0.882 average_precision: 0.603 roc_auc: 0.800 recall_macro: 0.835 f1_macro: 0.789
+Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
+accuracy: 0.903 average_precision: 0.782 roc_auc: 0.066 recall_macro: 0.854 f1_macro: 0.820
+Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
+accuracy: 0.913 average_precision: 0.762 roc_auc: 0.083 recall_macro: 0.853 f1_macro: 0.834
+
+Best model:
+DecisionTreeClassifier(class_weight='balanced', max_depth=1)
+Best Scores:
+accuracy: 0.872 average_precision: 0.519 roc_auc: 0.883 recall_macro: 0.883 f1_macro: 0.802