README.md

metadata

license: mit
library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-regression
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

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.

Click to expand

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.

Click to expand

[More Information Needed]

Model Card Authors

This model card is written by following authors:

Brenden Connors