8000 DOC added links to plot_gradient_boosting_regularization.py and plot_gradient_boosting_categorical.py by Siniade · Pull Request #30749 · scikit-learn/scikit-learn · GitHub
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DOC added links to plot_gradient_boosting_regularization.py and plot_gradient_boosting_categorical.py #30749

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merged 5 commits into from
Feb 12, 2025
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DOC added links to plot_gradient_boosting_regularization.py and plot_gradient_boosting_categorical.py #30749

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c3f7f60
Linked 3 examples to the User Guide and API documentation
Siniade Feb 2, 2025
d2e04e0
Modify linked examples as per suggestions
Siniade Feb 4, 2025
97697f5
Move examples to new line
Siniade Feb 6, 2025
94fb5ed
move examples
adrinjalali Feb 12, 2025
d95548d
Merge remote-tracking branch 'upstream/main' into link-to-example
adrinjalali Feb 12, 2025
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4 changes: 4 additions & 0 deletions sklearn/ensemble/_gb.py
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Expand Up @@ -1152,6 +1152,10 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
There is a trade-off between learning_rate and n_estimators.
Values must be in the range `[0.0, inf)`.

For an example of the effects of this parameter and its interaction with
``subsample``, see
:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`.

n_estimators : int, default=100
The number of boosting stages to perform. Gradient boosting
is fairly robust to over-fitting so a large number usually
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3 changes: 2 additions & 1 deletion sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
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Expand Up @@ -1512,7 +1512,8 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
converted to floating point numbers. This means that categorical values
of 1.0 and 1 are treated as the same category.

Read more in the :ref:`User Guide <categorical_support_gbdt>`.
Read more in the :ref:`User Guide <categorical_support_gbdt>` and
:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`.

.. versionadded:: 0.24

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