--- library_name: sklearn tags: - sklearn - skops - tabular-classification widget: structuredData: attribute_0: - material_7 - material_7 - material_7 attribute_1: - material_6 - material_5 - material_6 attribute_2: - 6 - 6 - 6 attribute_3: - 9 - 6 - 9 loading: - 101.52 - 91.34 - 167.03 measurement_0: - 9 - 10 - 11 measurement_1: - 11 - 11 - 5 measurement_10: - 14.926 - 15.162 - 16.398 measurement_11: - 20.394 - 19.46 - 20.613 measurement_12: - 11.829 - 9.114 - 11.007 measurement_13: - 16.195 - 16.024 - 16.061 measurement_14: - 16.517 - 17.132 - 15.18 measurement_15: - 13.826 - 12.257 - 15.758 measurement_16: - 14.206 - 15.094 - .nan measurement_17: - 723.712 - 896.835 - 893.454 measurement_2: - 2 - 10 - 6 measurement_3: - 17.492 - 18.114 - 18.42 measurement_4: - 13.962 - 10.185 - 13.565 measurement_5: - 15.716 - 18.06 - 16.916 measurement_6: - 17.104 - 18.283 - 17.917 measurement_7: - 12.377 - 10.957 - 10.394 measurement_8: - 19.221 - 20.638 - 19.805 measurement_9: - 11.613 - 11.804 - 12.012 product_code: - E - D - E --- # Model description This is a DecisionTreeClassifier model built for Kaggle Tabular Playground Series August 2022, trained on supersoaker production failures dataset. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.
Click to expand | Hyperparameter | Value | |-----------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('transformation', ColumnTransformer(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'])])), ('model', DecisionTreeClassifier(max_depth=4))] | | verbose | False | | transformation | ColumnTransformer(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'])]) | | model | DecisionTreeClassifier(max_depth=4) | | transformation__n_jobs | | | transformation__remainder | drop | | transformation__sparse_threshold | 0.3 | | transformation__transformer_weights | | | 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'])] | | transformation__verbose | False | | transformation__verbose_feature_names_out | True | | transformation__loading_missing_value_imputer | SimpleImputer() | | transformation__numerical_missing_value_imputer | SimpleImputer() | | transformation__attribute_0_encoder | OneHotEncoder() | | transformation__attribute_1_encoder | OneHotEncoder() | | transformation__product_code_encoder | OneHotEncoder() | | transformation__loading_missing_value_imputer__add_indicator | False | | transformation__loading_missing_value_imputer__copy | True | | transformation__loading_missing_value_imputer__fill_value | | | transformation__loading_missing_value_imputer__missing_values | nan | | transformation__loading_missing_value_imputer__strategy | mean | | transformation__loading_missing_value_imputer__verbose | 0 | | transformation__numerical_missing_value_imputer__add_indicator | False | | transformation__numerical_missing_value_imputer__copy | True | | transformation__numerical_missing_value_imputer__fill_value | | | transformation__numerical_missing_value_imputer__missing_values | nan | | transformation__numerical_missing_value_imputer__strategy | mean | | transformation__numerical_missing_value_imputer__verbose | 0 | | transformation__attribute_0_encoder__categories | auto | | transformation__attribute_0_encoder__drop | | | transformation__attribute_0_encoder__dtype | | | transformation__attribute_0_encoder__handle_unknown | error | | transformation__attribute_0_encoder__sparse | True | | transformation__attribute_1_encoder__categories | auto | | transformation__attribute_1_encoder__drop | | | transformation__attribute_1_encoder__dtype | | | transformation__attribute_1_encoder__handle_unknown | error | | transformation__attribute_1_encoder__sparse | True | | transformation__product_code_encoder__categories | auto | | transformation__product_code_encoder__drop | | | transformation__product_code_encoder__dtype | | | transformation__product_code_encoder__handle_unknown | error | | transformation__product_code_encoder__sparse | True | | model__ccp_alpha | 0.0 | | model__class_weight | | | model__criterion | gini | | model__max_depth | 4 | | model__max_features | | | model__max_leaf_nodes | | | model__min_impurity_decrease | 0.0 | | model__min_samples_leaf | 1 | | model__min_samples_split | 2 | | model__min_weight_fraction_leaf | 0.0 | | model__random_state | | | model__splitter | best |
### Model Plot The model plot is below.
Pipeline(steps=[('transformation',ColumnTransformer(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'])])),('model', DecisionTreeClassifier(max_depth=4))])
Please rerun this cell to show the HTML repr or trust the notebook.
## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|----------| | accuracy | 0.791961 | | f1 score | 0.791961 | # How to Get Started with the Model Use the code below to get started with the model.
Click to expand ```python import pickle with open(decision-tree-playground-kaggle/model.pkl, 'rb') as file: clf = pickle.load(file) ```
# Model Card Authors This model card is written by following authors: huggingface # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # Additional Content ## Tree Plot ![Tree Plot](tree.png) ## Confusion Matrix ![Confusion Matrix](confusion_matrix.png)