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pushing files to the repo from the example!

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  1. README.md +283 -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_6
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+ - material_5
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+ - material_6
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+ attribute_2:
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+ - 6
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+ - 6
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+ - 6
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+ attribute_3:
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+ - 9
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+ - 6
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+ - 9
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+ loading:
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+ - 101.52
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+ - 91.34
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+ - 167.03
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+ measurement_0:
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+ - 9
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+ - 10
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+ - 11
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+ measurement_1:
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+ - 11
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+ - 11
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+ - 5
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+ measurement_10:
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+ - 14.926
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+ - 15.162
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+ - 16.398
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+ measurement_11:
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+ - 20.394
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+ - 19.46
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+ - 20.613
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+ measurement_12:
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+ - 11.829
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+ - 9.114
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+ - 11.007
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+ measurement_13:
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+ - 16.195
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+ - 16.024
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+ - 16.061
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+ measurement_14:
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+ - 16.517
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+ - 17.132
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+ - 15.18
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+ measurement_15:
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+ - 13.826
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+ - 12.257
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+ - 15.758
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+ measurement_16:
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+ - 14.206
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+ - 15.094
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+ - .nan
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+ measurement_17:
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+ - 723.712
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+ - 896.835
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+ - 893.454
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+ measurement_2:
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+ - 2
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+ - 10
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+ - 6
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+ measurement_3:
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+ - 17.492
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+ - 18.114
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+ - 18.42
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+ measurement_4:
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+ - 13.962
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+ - 10.185
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+ - 13.565
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+ measurement_5:
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+ - 15.716
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+ - 18.06
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+ - 16.916
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+ measurement_6:
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+ - 17.104
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+ - 18.283
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+ - 17.917
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+ measurement_7:
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+ - 12.377
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+ - 10.957
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+ - 10.394
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+ measurement_8:
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+ - 19.221
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+ - 20.638
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+ - 19.805
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+ measurement_9:
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+ - 11.613
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+ - 11.804
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+ - 12.012
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+ product_code:
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+ - E
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+ - D
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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-b5518c10-fd7e-49af-b124-60d3dd3d0f86 {color: black;background-color: white;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 pre{padding: 0;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-toggleable {background-color: white;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 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-b5518c10-fd7e-49af-b124-60d3dd3d0f86 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-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-estimator:hover {background-color: #d4ebff;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-item {z-index: 1;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-parallel-item:only-child::after {width: 0;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 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-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86 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-b5518c10-fd7e-49af-b124-60d3dd3d0f86 div.sk-text-repr-fallback {display: none;}</style><div id="sk-b5518c10-fd7e-49af-b124-60d3dd3d0f86" 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="48fbfeb0-e954-46f7-9a36-8dfe86284fca" type="checkbox" ><label for="48fbfeb0-e954-46f7-9a36-8dfe86284fca" 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="157828b7-30d1-4b5b-b25e-971143379fff" type="checkbox" ><label for="157828b7-30d1-4b5b-b25e-971143379fff" 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="3bde7e44-3687-4b99-a3b7-b4e87023ec85" type="checkbox" ><label for="3bde7e44-3687-4b99-a3b7-b4e87023ec85" 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="ef9279cb-7d77-4ef1-aafe-26e433e2a615" type="checkbox" ><label for="ef9279cb-7d77-4ef1-aafe-26e433e2a615" 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="b079e8d7-f789-4622-ad66-197193ef0061" type="checkbox" ><label for="b079e8d7-f789-4622-ad66-197193ef0061" 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="969f6026-8077-468a-b332-8ceb69bac4e9" type="checkbox" ><label for="969f6026-8077-468a-b332-8ceb69bac4e9" 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="5bb6cc8f-c971-47b8-a1bc-fe8053602d5c" type="checkbox" ><label for="5bb6cc8f-c971-47b8-a1bc-fe8053602d5c" 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="8a841657-38e1-41bb-b8f9-5ad2cc25f7d3" type="checkbox" ><label for="8a841657-38e1-41bb-b8f9-5ad2cc25f7d3" 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="be08add7-98fc-40b5-a259-d462d738780a" type="checkbox" ><label for="be08add7-98fc-40b5-a259-d462d738780a" 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="cf07a6c2-b92e-40b1-9862-2c1ca3baab47" type="checkbox" ><label for="cf07a6c2-b92e-40b1-9862-2c1ca3baab47" 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="244735dc-f1e1-458c-a1c6-60ef847b9cae" type="checkbox" ><label for="244735dc-f1e1-458c-a1c6-60ef847b9cae" 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="2f1a1c41-e1c4-40ce-afd9-9658030b3423" type="checkbox" ><label for="2f1a1c41-e1c4-40ce-afd9-9658030b3423" 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="25044b48-b814-45f9-a75b-9ee472bdc79c" type="checkbox" ><label for="25044b48-b814-45f9-a75b-9ee472bdc79c" 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 | 0.791961 |
234
+ | f1 score | 0.791961 |
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
+ # Additional Content
276
+
277
+ ## Tree Plot
278
+
279
+ ![Tree Plot](decision-tree-playground-kaggle/tree.png)
280
+
281
+ ## Confusion Matrix
282
+
283
+ ![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",
5
+ "loading",
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+ "attribute_0",
7
+ "attribute_1",
8
+ "attribute_2",
9
+ "attribute_3",
10
+ "measurement_0",
11
+ "measurement_1",
12
+ "measurement_2",
13
+ "measurement_3",
14
+ "measurement_4",
15
+ "measurement_5",
16
+ "measurement_6",
17
+ "measurement_7",
18
+ "measurement_8",
19
+ "measurement_9",
20
+ "measurement_10",
21
+ "measurement_11",
22
+ "measurement_12",
23
+ "measurement_13",
24
+ "measurement_14",
25
+ "measurement_15",
26
+ "measurement_16",
27
+ "measurement_17"
28
+ ],
29
+ "environment": [
30
+ "scikit-learn=1.0.2"
31
+ ],
32
+ "example_input": {
33
+ "attribute_0": [
34
+ "material_7",
35
+ "material_7",
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+ "material_7"
37
+ ],
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+ "attribute_1": [
39
+ "material_6",
40
+ "material_5",
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+ "material_6"
42
+ ],
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+ "attribute_2": [
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+ 6,
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+ 6,
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+ 6
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+ ],
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+ "attribute_3": [
49
+ 9,
50
+ 6,
51
+ 9
52
+ ],
53
+ "loading": [
54
+ 101.52,
55
+ 91.34,
56
+ 167.03
57
+ ],
58
+ "measurement_0": [
59
+ 9,
60
+ 10,
61
+ 11
62
+ ],
63
+ "measurement_1": [
64
+ 11,
65
+ 11,
66
+ 5
67
+ ],
68
+ "measurement_10": [
69
+ 14.926,
70
+ 15.162,
71
+ 16.398
72
+ ],
73
+ "measurement_11": [
74
+ 20.394,
75
+ 19.46,
76
+ 20.613
77
+ ],
78
+ "measurement_12": [
79
+ 11.829,
80
+ 9.114,
81
+ 11.007
82
+ ],
83
+ "measurement_13": [
84
+ 16.195,
85
+ 16.024,
86
+ 16.061
87
+ ],
88
+ "measurement_14": [
89
+ 16.517,
90
+ 17.132,
91
+ 15.18
92
+ ],
93
+ "measurement_15": [
94
+ 13.826,
95
+ 12.257,
96
+ 15.758
97
+ ],
98
+ "measurement_16": [
99
+ 14.206,
100
+ 15.094,
101
+ NaN
102
+ ],
103
+ "measurement_17": [
104
+ 723.712,
105
+ 896.835,
106
+ 893.454
107
+ ],
108
+ "measurement_2": [
109
+ 2,
110
+ 10,
111
+ 6
112
+ ],
113
+ "measurement_3": [
114
+ 17.492,
115
+ 18.114,
116
+ 18.42
117
+ ],
118
+ "measurement_4": [
119
+ 13.962,
120
+ 10.185,
121
+ 13.565
122
+ ],
123
+ "measurement_5": [
124
+ 15.716,
125
+ 18.06,
126
+ 16.916
127
+ ],
128
+ "measurement_6": [
129
+ 17.104,
130
+ 18.283,
131
+ 17.917
132
+ ],
133
+ "measurement_7": [
134
+ 12.377,
135
+ 10.957,
136
+ 10.394
137
+ ],
138
+ "measurement_8": [
139
+ 19.221,
140
+ 20.638,
141
+ 19.805
142
+ ],
143
+ "measurement_9": [
144
+ 11.613,
145
+ 11.804,
146
+ 12.012
147
+ ],
148
+ "product_code": [
149
+ "E",
150
+ "D",
151
+ "E"
152
+ ]
153
+ },
154
+ "model": {
155
+ "file": "model.pkl"
156
+ },
157
+ "task": "tabular-classification"
158
+ }
159
+ }
confusion_matrix.png ADDED
model.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:72099d3816c44c13b2284469de690419a7326caef2c0401ab91a37e7c8c4348e
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+ size 6824
tree.png ADDED