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Commit afd4ad8

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author
Preyas Shah
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Additional links for inspection examples
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‎examples/inspection/plot_partial_dependence.py

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# However, it is worth noting that we are creating potential meaningless
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# synthetic samples if features are correlated.
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#
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# .. _ice-vs-pdp:
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#
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# ICE vs. PDP
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# ~~~~~~~~~~~
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# -----------
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#
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# PDP is an average of the marginal effects of the features. We are averaging the
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# response of all samples of the provided set. Thus, some effects could be hidden. In
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# this regard, it is possible to plot each individual response. This representation is

‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py

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@@ -1562,6 +1562,8 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
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and specifies that each branch of a tree will either only split
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on features 0 and 1 or only split on features 2, 3 and 4.
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See :ref:`Example<ice-vs-pdp>` for how to use `interaction_cst`.
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.. versionadded:: 1.2
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warm_start : bool, default=False

‎sklearn/inspection/_partial_dependence.py

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the average response of an estimator for each possible value of the
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feature.
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Read more in the :ref:`User Guide <partial_dependence>`.
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Read more in
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:ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
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and the :ref:`User Guide <partial_dependence>`.
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.. warning::
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‎sklearn/inspection/_plot/partial_dependence.py

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marks on the x-axes for one-way plots, and on both axes for two-way
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plots.
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Read more in the :ref:`User Guide <partial_dependence>`.
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Read more in
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:ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
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and the :ref:`User Guide <partial_dependence>`.
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.. note::
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