Model description
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Intended uses & limitations
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Training Procedure
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Hyperparameters
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Hyperparameter | Value |
---|---|
memory | |
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))] |
verbose | False |
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) |
columntransformer__n_jobs | |
columntransformer__remainder | passthrough |
columntransformer__sparse_threshold | 0.3 |
columntransformer__transformer_weights | |
columntransformer__transformers | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False), <sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20>)] |
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 | <class 'numpy.float64'> |
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))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
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))])
ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20>)])
<sklearn.compose._column_transformer.make_column_selector object at 0x7c049c39ec20>
OneHotEncoder(handle_unknown='ignore', sparse=False)
['Length1', 'Length2', 'Length3', 'Height', 'Width']
passthrough
GradientBoostingRegressor(random_state=42)
Evaluation Results
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Model Card Authors
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Citation
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model_card_authors
JP
limitations
This model is intended for educational purposes.
model_description
This is a GradientBoostingRegressor on a fish dataset.
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