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pushing files to the repo from the example!

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  1. README.md +281 -0
  2. config.json +159 -0
  3. confusion_matrix.png +0 -0
  4. model.pkl +3 -0
  5. tree.png +0 -0
README.md ADDED
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+ ---
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+ library_name: sklearn
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+ tags:
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+ - sklearn
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+ - skops
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+ - tabular-classification
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+ widget:
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+ structuredData:
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+ attribute_0:
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+ - material_7
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+ - material_7
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+ - material_7
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+ attribute_1:
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+ - material_8
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+ - material_6
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+ - material_8
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+ attribute_2:
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+ - 9
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+ - 6
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+ - 5
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+ attribute_3:
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+ - 5
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+ - 9
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+ - 8
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+ loading:
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+ - 119.49
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+ - 85.36
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+ - 73.71
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+ measurement_0:
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+ - 11
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+ - 10
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+ - 24
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+ measurement_1:
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+ - 2
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+ - 8
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+ - 7
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+ measurement_10:
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+ - 17.138
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+ - 15.632
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+ - 15.854
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+ measurement_11:
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+ - 19.954
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+ - 18.992
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+ - 20.405
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+ measurement_12:
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+ - 12.348
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+ - .nan
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+ - 13.638
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+ measurement_13:
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+ - 13.93
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+ - 15.148
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+ - .nan
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+ measurement_14:
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+ - 15.889
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+ - .nan
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+ - 15.854
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+ measurement_15:
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+ - 15.831
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+ - 15.849
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+ - 16.555
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+ measurement_16:
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+ - 16.102
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+ - 15.896
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+ - 17.145
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+ measurement_17:
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+ - 643.509
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+ - 722.585
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+ - 802.57
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+ measurement_2:
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+ - 3
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+ - 3
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+ - 7
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+ measurement_3:
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+ - 17.659
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+ - 19.679
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+ - 17.291
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+ measurement_4:
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+ - 11.578
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+ - 11.49
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+ - 11.691
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+ measurement_5:
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+ - 15.514
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+ - 18.267
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+ - 18.289
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+ measurement_6:
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+ - 15.99
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+ - 17.921
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+ - 17.396
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+ measurement_7:
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+ - 12.231
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+ - 11.978
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+ - 11.361
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+ measurement_8:
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+ - 19.92
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+ - 18.135
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+ - 19.67
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+ measurement_9:
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+ - 10.555
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+ - 11.113
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+ - 11.375
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+ product_code:
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+ - A
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+ - E
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+ - C
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+ ---
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+
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+ # Model description
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+
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+ This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset.
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+
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+ ## Intended uses & limitations
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+
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+ This model is not ready to be used in production.
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+
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+ ## Training Procedure
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+
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+ ### Hyperparameters
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+
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+ The model is trained with below hyperparameters.
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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+ | Hyperparameter | Value |
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+ |-----------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | memory | |
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+ | steps | [('transformation', ColumnTransformer(transformers=[('loading_missing_value_imputer',
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+ SimpleImputer(), ['loading']),
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+ ('numerical_missing_value_imputer',
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+ SimpleImputer(),
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+ ['loading', 'measurement_3', 'measurement_4',
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+ 'measurement_5', 'measurement_6',
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+ 'measurement_7', 'measurement_8',
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+ 'measurement_9', 'measurement_10',
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+ 'measurement_11', 'measurement_12',
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+ 'measurement_13', 'measurement_14',
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+ 'measurement_15', 'measurement_16',
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+ 'measurement_17']),
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+ ('attribute_0_encoder', OneHotEncoder(),
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+ ['attribute_0']),
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+ ('attribute_1_encoder', OneHotEncoder(),
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+ ['attribute_1']),
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+ ('product_code_encoder', OneHotEncoder(),
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+ ['product_code'])])), ('model', DecisionTreeClassifier(max_depth=4))] |
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+ | verbose | False |
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+ | transformation | ColumnTransformer(transformers=[('loading_missing_value_imputer',
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+ SimpleImputer(), ['loading']),
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+ ('numerical_missing_value_imputer',
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+ SimpleImputer(),
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+ ['loading', 'measurement_3', 'measurement_4',
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+ 'measurement_5', 'measurement_6',
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+ 'measurement_7', 'measurement_8',
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+ 'measurement_9', 'measurement_10',
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+ 'measurement_11', 'measurement_12',
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+ 'measurement_13', 'measurement_14',
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+ 'measurement_15', 'measurement_16',
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+ 'measurement_17']),
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+ ('attribute_0_encoder', OneHotEncoder(),
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+ ['attribute_0']),
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+ ('attribute_1_encoder', OneHotEncoder(),
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+ ['attribute_1']),
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+ ('product_code_encoder', OneHotEncoder(),
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+ ['product_code'])]) |
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+ | model | DecisionTreeClassifier(max_depth=4) |
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+ | transformation__n_jobs | |
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+ | transformation__remainder | drop |
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+ | transformation__sparse_threshold | 0.3 |
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+ | transformation__transformer_weights | |
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+ | transformation__transformers | [('loading_missing_value_imputer', SimpleImputer(), ['loading']), ('numerical_missing_value_imputer', SimpleImputer(), ['loading', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']), ('attribute_0_encoder', OneHotEncoder(), ['attribute_0']), ('attribute_1_encoder', OneHotEncoder(), ['attribute_1']), ('product_code_encoder', OneHotEncoder(), ['product_code'])] |
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+ | transformation__verbose | False |
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+ | transformation__verbose_feature_names_out | True |
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+ | transformation__loading_missing_value_imputer | SimpleImputer() |
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+ | transformation__numerical_missing_value_imputer | SimpleImputer() |
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+ | transformation__attribute_0_encoder | OneHotEncoder() |
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+ | transformation__attribute_1_encoder | OneHotEncoder() |
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+ | transformation__product_code_encoder | OneHotEncoder() |
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+ | transformation__loading_missing_value_imputer__add_indicator | False |
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+ | transformation__loading_missing_value_imputer__copy | True |
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+ | transformation__loading_missing_value_imputer__fill_value | |
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+ | transformation__loading_missing_value_imputer__missing_values | nan |
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+ | transformation__loading_missing_value_imputer__strategy | mean |
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+ | transformation__loading_missing_value_imputer__verbose | 0 |
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+ | transformation__numerical_missing_value_imputer__add_indicator | False |
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+ | transformation__numerical_missing_value_imputer__copy | True |
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+ | transformation__numerical_missing_value_imputer__fill_value | |
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+ | transformation__numerical_missing_value_imputer__missing_values | nan |
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+ | transformation__numerical_missing_value_imputer__strategy | mean |
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+ | transformation__numerical_missing_value_imputer__verbose | 0 |
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+ | transformation__attribute_0_encoder__categories | auto |
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+ | transformation__attribute_0_encoder__drop | |
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+ | transformation__attribute_0_encoder__dtype | <class 'numpy.float64'> |
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+ | transformation__attribute_0_encoder__handle_unknown | error |
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+ | transformation__attribute_0_encoder__sparse | True |
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+ | transformation__attribute_1_encoder__categories | auto |
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+ | transformation__attribute_1_encoder__drop | |
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+ | transformation__attribute_1_encoder__dtype | <class 'numpy.float64'> |
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+ | transformation__attribute_1_encoder__handle_unknown | error |
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+ | transformation__attribute_1_encoder__sparse | True |
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+ | transformation__product_code_encoder__categories | auto |
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+ | transformation__product_code_encoder__drop | |
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+ | transformation__product_code_encoder__dtype | <class 'numpy.float64'> |
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+ | transformation__product_code_encoder__handle_unknown | error |
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+ | transformation__product_code_encoder__sparse | True |
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+ | model__ccp_alpha | 0.0 |
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+ | model__class_weight | |
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+ | model__criterion | gini |
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+ | model__max_depth | 4 |
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+ | model__max_features | |
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+ | model__max_leaf_nodes | |
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+ | model__min_impurity_decrease | 0.0 |
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+ | model__min_samples_leaf | 1 |
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+ | model__min_samples_split | 2 |
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+ | model__min_weight_fraction_leaf | 0.0 |
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+ | model__random_state | |
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+ | model__splitter | best |
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+
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+ </details>
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+
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+ ### Model Plot
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+
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+ The model plot is below.
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+
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+ <style>#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 {color: black;background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 pre{padding: 0;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-toggleable {background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 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-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 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-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-item {z-index: 1;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item:only-child::after {width: 0;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 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;position: relative;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 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-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-text-repr-fallback {display: none;}</style><div id="sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;transformation&#x27;,ColumnTransformer(transformers=[(&#x27;loading_missing_value_imputer&#x27;,SimpleImputer(),[&#x27;loading&#x27;]),(&#x27;numerical_missing_value_imputer&#x27;,SimpleImputer(),[&#x27;loading&#x27;, &#x27;measurement_3&#x27;,&#x27;measurement_4&#x27;,&#x27;measurement_5&#x27;,&#x27;measurement_6&#x27;,&#x27;measurement_7&#x27;,&#x27;measurement_8&#x27;,&#x27;measurement_9&#x27;,&#x27;measurement_10&#x27;,&#x27;measurement_11&#x27;,&#x27;measurement_12&#x27;,&#x27;measurement_13&#x27;,&#x27;measurement_14&#x27;,&#x27;measurement_15&#x27;,&#x27;measurement_16&#x27;,&#x27;measurement_17&#x27;]),(&#x27;attribute_0_encoder&#x27;,OneHotEncoder(),[&#x27;attribute_0&#x27;]),(&#x27;attribute_1_encoder&#x27;,OneHotEncoder(),[&#x27;attribute_1&#x27;]),(&#x27;product_code_encoder&#x27;,OneHotEncoder(),[&#x27;product_code&#x27;])])),(&#x27;model&#x27;, DecisionTreeClassifier(max_depth=4))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</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="82f19dd0-da3e-499c-84b9-f67ed489906d" type="checkbox" ><label for="82f19dd0-da3e-499c-84b9-f67ed489906d" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;transformation&#x27;,ColumnTransformer(transformers=[(&#x27;loading_missing_value_imputer&#x27;,SimpleImputer(),[&#x27;loading&#x27;]),(&#x27;numerical_missing_value_imputer&#x27;,SimpleImputer(),[&#x27;loading&#x27;, &#x27;measurement_3&#x27;,&#x27;measurement_4&#x27;,&#x27;measurement_5&#x27;,&#x27;measurement_6&#x27;,&#x27;measurement_7&#x27;,&#x27;measurement_8&#x27;,&#x27;measurement_9&#x27;,&#x27;measurement_10&#x27;,&#x27;measurement_11&#x27;,&#x27;measurement_12&#x27;,&#x27;measurement_13&#x27;,&#x27;measurement_14&#x27;,&#x27;measurement_15&#x27;,&#x27;measurement_16&#x27;,&#x27;measurement_17&#x27;]),(&#x27;attribute_0_encoder&#x27;,OneHotEncoder(),[&#x27;attribute_0&#x27;]),(&#x27;attribute_1_encoder&#x27;,OneHotEncoder(),[&#x27;attribute_1&#x27;]),(&#x27;product_code_encoder&#x27;,OneHotEncoder(),[&#x27;product_code&#x27;])])),(&#x27;model&#x27;, DecisionTreeClassifier(max_depth=4))])</pre></div></div></div><div class="sk-serial"><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="e3bc6996-eefc-4601-a7df-7850743b36d6" type="checkbox" ><label for="e3bc6996-eefc-4601-a7df-7850743b36d6" class="sk-toggleable__label sk-toggleable__label-arrow">transformation: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;loading_missing_value_imputer&#x27;,SimpleImputer(), [&#x27;loading&#x27;]),(&#x27;numerical_missing_value_imputer&#x27;,SimpleImputer(),[&#x27;loading&#x27;, &#x27;measurement_3&#x27;, &#x27;measurement_4&#x27;,&#x27;measurement_5&#x27;, &#x27;measurement_6&#x27;,&#x27;measurement_7&#x27;, &#x27;measurement_8&#x27;,&#x27;measurement_9&#x27;, &#x27;measurement_10&#x27;,&#x27;measurement_11&#x27;, &#x27;measurement_12&#x27;,&#x27;measurement_13&#x27;, &#x27;measurement_14&#x27;,&#x27;measurement_15&#x27;, &#x27;measurement_16&#x27;,&#x27;measurement_17&#x27;]),(&#x27;attribute_0_encoder&#x27;, OneHotEncoder(),[&#x27;attribute_0&#x27;]),(&#x27;attribute_1_encoder&#x27;, OneHotEncoder(),[&#x27;attribute_1&#x27;]),(&#x27;product_code_encoder&#x27;, OneHotEncoder(),[&#x27;product_code&#x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="9cc3e3ef-17e8-4bf4-a121-0a29a377373e" type="checkbox" ><label for="9cc3e3ef-17e8-4bf4-a121-0a29a377373e" class="sk-toggleable__label sk-toggleable__label-arrow">loading_missing_value_imputer</label><div class="sk-toggleable__content"><pre>[&#x27;loading&#x27;]</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="e7ba5c2d-3bf3-444f-be00-0ecf4b563cd7" type="checkbox" ><label for="e7ba5c2d-3bf3-444f-be00-0ecf4b563cd7" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="2277368d-30f2-46c1-a283-9f0ccf350872" type="checkbox" ><label for="2277368d-30f2-46c1-a283-9f0ccf350872" class="sk-toggleable__label sk-toggleable__label-arrow">numerical_missing_value_imputer</label><div class="sk-toggleable__content"><pre>[&#x27;loading&#x27;, &#x27;measurement_3&#x27;, &#x27;measurement_4&#x27;, &#x27;measurement_5&#x27;, &#x27;measurement_6&#x27;, &#x27;measurement_7&#x27;, &#x27;measurement_8&#x27;, &#x27;measurement_9&#x27;, &#x27;measurement_10&#x27;, &#x27;measurement_11&#x27;, &#x27;measurement_12&#x27;, &#x27;measurement_13&#x27;, &#x27;measurement_14&#x27;, &#x27;measurement_15&#x27;, &#x27;measurement_16&#x27;, &#x27;measurement_17&#x27;]</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="2a49159e-c23f-4cbe-92bb-09bb64c1354d" type="checkbox" ><label for="2a49159e-c23f-4cbe-92bb-09bb64c1354d" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c87d52bb-0b23-4e43-abe8-afc3759dac02" type="checkbox" ><label for="c87d52bb-0b23-4e43-abe8-afc3759dac02" class="sk-toggleable__label sk-toggleable__label-arrow">attribute_0_encoder</label><div class="sk-toggleable__content"><pre>[&#x27;attribute_0&#x27;]</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="023971df-ed99-4eaf-8f0d-cd115bacbb45" type="checkbox" ><label for="023971df-ed99-4eaf-8f0d-cd115bacbb45" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="111f5303-3f63-409a-9dc1-74ab94419974" type="checkbox" ><label for="111f5303-3f63-409a-9dc1-74ab94419974" class="sk-toggleable__label sk-toggleable__label-arrow">attribute_1_encoder</label><div class="sk-toggleable__content"><pre>[&#x27;attribute_1&#x27;]</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="c858e1b1-b68f-4700-9111-32772a7b51ab" type="checkbox" ><label for="c858e1b1-b68f-4700-9111-32772a7b51ab" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="5ce65801-d4be-48d4-81d3-7998e483cf65" type="checkbox" ><label for="5ce65801-d4be-48d4-81d3-7998e483cf65" class="sk-toggleable__label sk-toggleable__label-arrow">product_code_encoder</label><div class="sk-toggleable__content"><pre>[&#x27;product_code&#x27;]</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="75a1cce7-c7f7-41cf-bb4d-8e403291a41b" type="checkbox" ><label for="75a1cce7-c7f7-41cf-bb4d-8e403291a41b" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3c311565-4080-492c-b353-fbc41e1c17d5" type="checkbox" ><label for="3c311565-4080-492c-b353-fbc41e1c17d5" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(max_depth=4)</pre></div></div></div></div></div></div></div>
224
+
225
+ ## Evaluation Results
226
+
227
+ You can find the details about evaluation process and the evaluation results.
228
+
229
+
230
+
231
+ | Metric | Value |
232
+ |----------|----------|
233
+ | accuracy | 0.786392 |
234
+ | f1 score | 0.786392 |
235
+
236
+ # How to Get Started with the Model
237
+
238
+ Use the code below to get started with the model.
239
+
240
+ <details>
241
+ <summary> Click to expand </summary>
242
+
243
+ ```python
244
+ import pickle
245
+ with open(decision-tree-playground-kaggle/model.pkl, 'rb') as file:
246
+ clf = pickle.load(file)
247
+ ```
248
+
249
+ </details>
250
+
251
+
252
+
253
+
254
+ # Model Card Authors
255
+
256
+ This model card is written by following authors:
257
+
258
+ huggingface
259
+
260
+ # Model Card Contact
261
+
262
+ You can contact the model card authors through following channels:
263
+ [More Information Needed]
264
+
265
+ # Citation
266
+
267
+ Below you can find information related to citation.
268
+
269
+ **BibTeX:**
270
+ ```
271
+ [More Information Needed]
272
+ ```
273
+
274
+
275
+ Tree Plot
276
+ ![Tree Plot](decision-tree-playground-kaggle/tree.png)
277
+
278
+
279
+
280
+ Confusion Matrix
281
+ ![Confusion Matrix](decision-tree-playground-kaggle/confusion_matrix.png)
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+ {
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+ "sklearn": {
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+ "columns": [
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+ "product_code",
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+ "loading",
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+ "attribute_0",
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+ "attribute_1",
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+ "attribute_2",
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+ "attribute_3",
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+ "measurement_0",
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+ "measurement_1",
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+ "measurement_2",
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+ "measurement_3",
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+ "measurement_4",
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+ "measurement_5",
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+ "measurement_6",
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+ "measurement_7",
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+ "measurement_8",
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+ "measurement_9",
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+ "measurement_10",
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+ "measurement_11",
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+ "measurement_12",
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+ "measurement_13",
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+ "measurement_14",
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+ "measurement_15",
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+ "measurement_16",
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+ "measurement_17"
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+ ],
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+ "environment": [
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+ "scikit-learn=1.0.2"
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+ ],
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+ "example_input": {
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+ "attribute_0": [
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+ "material_7",
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+ "material_7",
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+ "material_7"
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+ ],
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+ "attribute_1": [
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+ "material_8",
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+ "material_6",
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+ "material_8"
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+ ],
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+ "attribute_2": [
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+ 9,
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+ 6,
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+ 5
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+ ],
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+ "attribute_3": [
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+ 5,
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+ 9,
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+ 8
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+ ],
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+ "loading": [
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+ 119.49,
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+ 85.36,
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+ 73.71
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+ ],
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+ "measurement_0": [
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+ 11,
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+ 10,
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+ 24
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+ ],
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+ "measurement_1": [
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+ 2,
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+ 8,
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+ 7
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+ ],
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+ "measurement_10": [
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+ 17.138,
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+ 15.632,
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+ ],
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+ "measurement_11": [
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+ 19.954,
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+ 18.992,
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+ 20.405
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+ ],
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+ "measurement_12": [
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+ 12.348,
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+ NaN,
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+ 13.638
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+ ],
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+ "measurement_13": [
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+ 13.93,
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+ 15.148,
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+ NaN
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+ ],
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+ "measurement_14": [
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+ 15.889,
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+ NaN,
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+ 15.854
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+ ],
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+ "measurement_15": [
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+ 15.831,
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+ ],
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+ "measurement_16": [
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+ 16.102,
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+ ],
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+ "measurement_17": [
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+ 643.509,
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+ ],
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+ "measurement_2": [
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+ 3,
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+ ],
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+ "measurement_3": [
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+ 17.659,
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+ 19.679,
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+ 17.291
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+ ],
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+ "measurement_4": [
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+ 11.578,
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+ ],
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+ "measurement_5": [
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+ 15.514,
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+ ],
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+ "measurement_6": [
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+ 15.99,
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+ ],
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+ "measurement_7": [
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+ 12.231,
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+ ],
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+ "measurement_8": [
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+ 19.92,
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+ ],
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+ "measurement_9": [
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+ 10.555,
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+ ],
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+ "product_code": [
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+ "A",
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+ "E",
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+ "C"
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+ ]
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+ },
154
+ "model": {
155
+ "file": "model.pkl"
156
+ },
157
+ "task": "tabular-classification"
158
+ }
159
+ }
confusion_matrix.png ADDED
model.pkl ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:0b657336bbde87de9beb4cffee3e500e48379fca8417bcdf538ceea6e6d59bb9
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+ size 6824
tree.png ADDED