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Model description

This is a GradientBoostingRegressor on a fish dataset.

Intended uses & limitations

This model is intended for educational purposes.

Hyperparameters

The model is trained with below 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 0x000001E750BBC6A0>)])), ('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 0x000001E750BBC6A0>)])
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 0x000001E750BBC6A0>)]
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__handle_unknown ignore
columntransformer__onehotencoder__sparse False
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

The model plot is below.

Pipeline(steps=[('columntransformer',ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])),('gradientboostingregressor',GradientBoostingRegressor(random_state=42))])
Please rerun this cell to show the HTML repr or trust the notebook.

How to Get Started with the Model

Use the code below to get started with the model.

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from skops.hub_utils import download
from skops.io import load

download("brendenc/Fish-Weight", "path_to_folder")
# make sure model file is in skops format
# if model is a pickle file, make sure it's from a source you trust
model = load("path_to_folder/example.pkl")

Model Card Authors

This model card is written by following authors:

Brenden Connors

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Space using scikit-learn/Fish-Weight 1