README.md

metadata

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
        - 23.9
      Length2:
        - 25.4
        - 26.3
        - 26.5
      Length3:
        - 30
        - 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

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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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Evaluation Results

[More Information Needed]

How to Get Started with the Model

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Model Card Authors

This model card is written by following authors:

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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:

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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.