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.

Training Procedure

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.

## Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value

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

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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