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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))])
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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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