--- library_name: sklearn license: mit tags: - sklearn - skops - tabular-regression model_format: pickle model_file: example.pkl widget: - structuredData: Height: - 11.52 - 12.48 - 12.3778 Length1: - 23.2 - 24.0 - 23.9 Length2: - 25.4 - 26.3 - 26.5 Length3: - 30.0 - 31.2 - 31.1 Species: - Bream - Bream - Bream Width: - 4.02 - 4.3056 - 4.6961 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure [More Information Needed] ### Hyperparameters
Click to expand | Hyperparameter | Value | |---------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('columntransformer', ColumnTransformer(remainder='passthrough',
transformers=[('onehotencoder',
OneHotEncoder(handle_unknown='ignore',
sparse=False),
)])), ('gradientboostingregressor', GradientBoostingRegressor(random_state=42))] | | verbose | False | | columntransformer | ColumnTransformer(remainder='passthrough',
transformers=[('onehotencoder',
OneHotEncoder(handle_unknown='ignore',
sparse=False),
)]) | | gradientboostingregressor | GradientBoostingRegressor(random_state=42) | | columntransformer__n_jobs | | | columntransformer__remainder | passthrough | | columntransformer__sparse_threshold | 0.3 | | columntransformer__transformer_weights | | | columntransformer__transformers | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False), )] | | columntransformer__verbose | False | | columntransformer__verbose_feature_names_out | True | | columntransformer__onehotencoder | OneHotEncoder(handle_unknown='ignore', sparse=False) | | columntransformer__onehotencoder__categories | auto | | columntransformer__onehotencoder__drop | | | columntransformer__onehotencoder__dtype | | | columntransformer__onehotencoder__feature_name_combiner | concat | | columntransformer__onehotencoder__handle_unknown | ignore | | columntransformer__onehotencoder__max_categories | | | columntransformer__onehotencoder__min_frequency | | | columntransformer__onehotencoder__sparse | False | | columntransformer__onehotencoder__sparse_output | True | | gradientboostingregressor__alpha | 0.9 | | gradientboostingregressor__ccp_alpha | 0.0 | | gradientboostingregressor__criterion | friedman_mse | | gradientboostingregressor__init | | | gradientboostingregressor__learning_rate | 0.1 | | gradientboostingregressor__loss | squared_error | | gradientboostingregressor__max_depth | 3 | | gradientboostingregressor__max_features | | | gradientboostingregressor__max_leaf_nodes | | | gradientboostingregressor__min_impurity_decrease | 0.0 | | gradientboostingregressor__min_samples_leaf | 1 | | gradientboostingregressor__min_samples_split | 2 | | gradientboostingregressor__min_weight_fraction_leaf | 0.0 | | gradientboostingregressor__n_estimators | 100 | | gradientboostingregressor__n_iter_no_change | | | gradientboostingregressor__random_state | 42 | | gradientboostingregressor__subsample | 1.0 | | gradientboostingregressor__tol | 0.0001 | | gradientboostingregressor__validation_fraction | 0.1 | | gradientboostingregressor__verbose | 0 | | gradientboostingregressor__warm_start | False |
### Model Plot 
Pipeline(steps=[('columntransformer',ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20>)])),('gradientboostingregressor',GradientBoostingRegressor(random_state=42))])
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## Evaluation Results [More Information Needed] # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # 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] ``` # model_card_authors JP # limitations This model is intended for educational purposes. # model_description This is a GradientBoostingRegressor on a fish dataset.