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DOC Make release highlights the first gallery section (#16952)
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‎doc/conf.py

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def __call__(self, directory):
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src_path = os.path.normpath(os.path.join(self.src_dir, directory))
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# Forces Release Highlights to the top
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if os.path.basename(src_path) == "release_highlights":
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return "0"
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readme = os.path.join(src_path, "README.txt")
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‎doc/developers/plotting.rst

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estimator.__class__.__name__)
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return viz.plot(ax=ax, name=name, **kwargs)
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Read more in :ref:`sphx_glr_auto_examples_plot_roc_curve_visualization_api.py`
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Read more in :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
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and the :ref:`User Guide <visualizations>`.
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Plotting with Multiple Axes

‎doc/modules/compose.rst

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* :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py`
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* :ref:`sphx_glr_auto_examples_model_selection_grid_search_text_feature_extraction.py`
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* :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py`
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* :ref:`sphx_glr_auto_examples_plot_kernel_approximation.py`
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py`
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* :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py`
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* :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`
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‎doc/modules/ensemble.rst

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* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_iris.py`
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* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`
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* :ref:`sphx_glr_auto_examples_plot_multioutput_face_completion.py`
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`
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.. topic:: References
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‎doc/modules/isotonic.rst

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for predicting to unseen data. The predictions of :class:`IsotonicRegression`
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thus form a function that is piecewise linear:
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.. figure:: ../auto_examples/images/sphx_glr_plot_isotonic_regression_001.png
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:target: ../auto_examples/plot_isotonic_regression.html
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.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_isotonic_regression_001.png
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:target: ../auto_examples/miscellaneous/plot_isotonic_regression.html
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:align: center

‎doc/modules/kernel_approximation.rst

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as :class:`Nystroem`. :class:`RBFSampler` is cheaper to compute, though, making
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use of larger feature spaces more efficient.
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.. figure:: ../auto_examples/images/sphx_glr_plot_kernel_approximation_002.png
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:target: ../auto_examples/plot_kernel_approximation.html
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.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_kernel_approximation_002.png
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:target: ../auto_examples/miscellaneous/plot_kernel_approximation.html
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:scale: 50%
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Comparing an exact RBF kernel (left) with the approximation (right)
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.. topic:: Examples:
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* :ref:`sphx_glr_auto_examples_plot_kernel_approximation.py`
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py`
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.. _additive_chi_kernel_approx:
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‎doc/modules/kernel_ridge.rst

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with :class:`~sklearn.svm.SVR` since it has learned a sparse model using only
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approximately 1/3 of the 100 training datapoints as support vectors.
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.. figure:: ../auto_examples/images/sphx_glr_plot_kernel_ridge_regression_001.png
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:target: ../auto_examples/plot_kernel_ridge_regression.html
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.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_kernel_ridge_regression_001.png
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The next figure compares the time for fitting and prediction of
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the :class:`~sklearn.svm.SVR`; :math:`\epsilon = 0` would correspond to a
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dense model.
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.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_kernel_ridge_regression_002.png
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‎doc/modules/multiclass.rst

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[i, j] indicates the presence of label j in sample i.
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.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_multilabel_001.png
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:target: ../auto_examples/miscellaneous/plot_multilabel.html
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.. topic:: Examples:
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* :ref:`sphx_glr_auto_examples_plot_multilabel.py`
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py`
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.. _ovo_classification:
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‎doc/modules/neighbors.rst

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The use of multi-output nearest neighbors for regression is demonstrated in
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:ref:`sphx_glr_auto_examples_plot_multioutput_face_completion.py`. In this example, the inputs
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:ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`. In this example, the inputs
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X are the pixels of the upper half of faces and the outputs Y are the pixels of
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the lower half of those faces.
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.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_multioutput_face_completion_001.png
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* :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`: an example of regression
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* :ref:`sphx_glr_auto_examples_plot_multioutput_face_completion.py`: an example of
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`: an example of
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‎doc/modules/outlier_detection.rst

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has no predict method to be applied on new data when it is used for outlier
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an ellipse. For more details on the different estimators refer to the example
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:ref:`sphx_glr_auto_examples_plot_anomaly_comparison.py` and the sections
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hereunder.
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:ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` and the
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.. topic:: Examples:
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* See :ref:`sphx_glr_auto_examples_plot_anomaly_comparison.py`
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* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
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* See :ref:`sphx_glr_auto_examples_plot_anomaly_comparison.py` for a
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* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
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for a comparison of :class:`ensemble.IsolationForest` with
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* See :ref:`sphx_glr_auto_examples_plot_anomaly_comparison.py` for a
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* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
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.. topic:: References:
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‎doc/modules/random_projection.rst

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.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_johnson_lindenstrauss_bound_001.png
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.. figure:: ../auto_examples/miscellaneous/images/sphx_glr_plot_johnson_lindenstrauss_bound_002.png
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:target: ../auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html
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* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py`
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‎doc/modules/tree.rst

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‎doc/modules/unsupervised_reduction.rst

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Feature agglomeration
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------------------------

‎doc/visualizations.rst

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* :ref:`sphx_glr_auto_examples_plot_display_object_visualization.py`
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py`
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* :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py`
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Available Plotting Utilities
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============================

‎examples/README.txt

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Examples
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========
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Miscellaneous examples
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----------------------
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Miscellaneous and introductory examples for scikit-learn.

‎examples/miscellaneous/README.txt

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.. _miscellaneous_examples:
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Miscellaneous
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-------------
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Miscellaneous and introductory examples for scikit-learn.
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‎examples/plot_anomaly_comparison.py renamed to ‎examples/miscellaneous/plot_anomaly_comparison.py

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‎examples/plot_changed_only_pprint_parameter.py renamed to ‎examples/miscellaneous/plot_changed_only_pprint_parameter.py

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‎examples/plot_display_object_visualization.py renamed to ‎examples/miscellaneous/plot_display_object_visualization.py

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‎examples/plot_isotonic_regression.py renamed to ‎examples/miscellaneous/plot_isotonic_regression.py

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‎examples/plot_johnson_lindenstrauss_bound.py renamed to ‎examples/miscellaneous/plot_johnson_lindenstrauss_bound.py

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‎examples/plot_kernel_ridge_regression.py renamed to ‎examples/miscellaneous/plot_kernel_ridge_regression.py

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‎examples/plot_partial_dependence_visualization_api.py renamed to ‎examples/miscellaneous/plot_partial_dependence_visualization_api.py

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‎sklearn/inspection/_plot/partial_dependence.py

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.. versionadded:: 0.22

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