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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_8
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+ - material_6
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+ attribute_2:
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+ - 5
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+ - 5
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+ - 6
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+ attribute_3:
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+ - 8
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+ - 8
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+ - 9
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+ loading:
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+ - 154.02
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+ - 108.73
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+ - 99.84
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+ measurement_0:
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+ - 14
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+ - 4
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+ - 6
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+ measurement_1:
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+ - 6
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+ - 7
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+ - 7
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+ measurement_10:
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+ - 16.637
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+ - 16.207
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+ - 17.17
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+ measurement_11:
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+ - 20.719
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+ - 20.058
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+ - 20.858
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+ measurement_12:
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+ - 12.824
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+ - 11.898
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+ - 10.968
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+ measurement_13:
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+ - 16.067
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+ - 13.871
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+ - 16.448
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+ measurement_14:
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+ - 15.181
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+ - 14.266
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+ - 15.6
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+ measurement_15:
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+ - 18.546
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+ - 15.734
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+ - 14.637
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+ measurement_16:
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+ - 19.402
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+ - 16.886
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+ - 13.86
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+ measurement_17:
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+ - 643.086
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+ - 642.533
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+ - 673.545
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+ measurement_2:
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+ - 6
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+ - 9
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+ - 6
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+ measurement_3:
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+ - 19.532
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+ - 18.128
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+ - NaN
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+ measurement_4:
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+ - 11.017
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+ - 11.866
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+ - 10.064
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+ measurement_5:
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+ - 15.639
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+ - 17.891
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+ - 16.287
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+ measurement_6:
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+ - 16.709
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+ - 20.302
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+ - 17.445
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+ measurement_7:
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+ - 10.057
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+ - NaN
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+ - 12.117
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+ measurement_8:
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+ - 20.201
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+ - 18.148
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+ - 20.659
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+ measurement_9:
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+ - 11.106
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+ - 10.221
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+ - 11.999
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+ product_code:
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+ - C
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+ - C
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+ - E
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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-b8914d13-cacb-404b-89fd-48f0ed8d671f {color: black;background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f pre{padding: 0;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-toggleable {background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f 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-b8914d13-cacb-404b-89fd-48f0ed8d671f 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-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-estimator:hover {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-item {z-index: 1;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-parallel-item:only-child::after {width: 0;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f 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-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-b8914d13-cacb-404b-89fd-48f0ed8d671f 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-b8914d13-cacb-404b-89fd-48f0ed8d671f div.sk-text-repr-fallback {display: none;}</style><div id="sk-b8914d13-cacb-404b-89fd-48f0ed8d671f" 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="fe201304-214c-493b-8896-11cea0894f6e" type="checkbox" ><label for="fe201304-214c-493b-8896-11cea0894f6e" 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="19136b49-925c-40a2-b4d1-37039bb014a9" type="checkbox" ><label for="19136b49-925c-40a2-b4d1-37039bb014a9" 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="c8ec7f92-b10a-41e7-b673-1239572ea00e" type="checkbox" ><label for="c8ec7f92-b10a-41e7-b673-1239572ea00e" 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="70fec50e-9c49-4818-a58f-ef8de932035c" type="checkbox" ><label for="70fec50e-9c49-4818-a58f-ef8de932035c" 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="ac8a6641-4222-4b12-b691-928201d9af73" type="checkbox" ><label for="ac8a6641-4222-4b12-b691-928201d9af73" 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="a14b63c1-fecb-445e-9a74-8229a531f0ea" type="checkbox" ><label for="a14b63c1-fecb-445e-9a74-8229a531f0ea" 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="80227cfc-e001-4c0d-b495-e4e0631a49d5" type="checkbox" ><label for="80227cfc-e001-4c0d-b495-e4e0631a49d5" 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="c52efc0c-08b7-467a-a0a1-f07cb6cecebc" type="checkbox" ><label for="c52efc0c-08b7-467a-a0a1-f07cb6cecebc" 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="6da0ab07-3d41-459c-a8a6-a56960b775f2" type="checkbox" ><label for="6da0ab07-3d41-459c-a8a6-a56960b775f2" 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="b515fbe5-466a-4eb7-84d9-35227a1e862a" type="checkbox" ><label for="b515fbe5-466a-4eb7-84d9-35227a1e862a" 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="72c4b8e6-3110-486f-8b33-a7db1f5e822f" type="checkbox" ><label for="72c4b8e6-3110-486f-8b33-a7db1f5e822f" 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="f3bfb5a1-317d-4ff4-8dd0-804ef1d7fd61" type="checkbox" ><label for="f3bfb5a1-317d-4ff4-8dd0-804ef1d7fd61" 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="dbcb65f9-3068-4263-9c1c-2e6413804681" type="checkbox" ><label for="dbcb65f9-3068-4263-9c1c-2e6413804681" 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>
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+
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+ ## Evaluation Results
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+
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+ You can find the details about evaluation process and the evaluation results.
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+
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+
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+
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+ | Metric | Value |
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+ |----------|---------|
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+ | accuracy | 0.7888 |
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+ | f1 score | 0.7888 |
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+
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+ # How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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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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+ ```python
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+ import pickle
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+ with open(decision-tree-playground-kaggle/model.pkl, 'rb') as file:
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+ clf = pickle.load(file)
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+ ```
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+
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+ </details>
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+
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+
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+
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+
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+ # Model Card Authors
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+
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+ This model card is written by following authors:
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+
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+ huggingface
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+
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+ # Model Card Contact
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+
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+ You can contact the model card authors through following channels:
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+ [More Information Needed]
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+
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+ # Citation
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+
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+ Below you can find information related to citation.
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+
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+ **BibTeX:**
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+ ```
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+ [More Information Needed]
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+ ```
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+
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+
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+ Tree Plot
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+ ![Tree Plot](tree.png)
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+
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+
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+
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+ Confusion Matrix
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+ ![Confusion Matrix](confusion_matrix.png)