.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/release_highlights/plot_release_highlights_1_3_0.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_3_0.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_3_0.py:


=======================================
Release Highlights for scikit-learn 1.3
=======================================

.. currentmodule:: sklearn

We are pleased to announce the release of scikit-learn 1.3! Many bug fixes
and improvements were added, as well as some new key features. We detail
below a few of the major features of this release. **For an exhaustive list of
all the changes**, please refer to the :ref:`release notes <release_notes_1_3>`.

To install the latest version (with pip)::

    pip install --upgrade scikit-learn

or with conda::

    conda install -c conda-forge scikit-learn

.. GENERATED FROM PYTHON SOURCE LINES 25-41

Metadata Routing
----------------
We are in the process of introducing a new way to route metadata such as
``sample_weight`` throughout the codebase, which would affect how
meta-estimators such as :class:`pipeline.Pipeline` and
:class:`model_selection.GridSearchCV` route metadata. While the
infrastructure for this feature is already included in this release, the work
is ongoing and not all meta-estimators support this new feature. You can read
more about this feature in the :ref:`Metadata Routing User Guide
<metadata_routing>`. Note that this feature is still under development and
not implemented for most meta-estimators.

Third party developers can already start incorporating this into their
meta-estimators. For more details, see
:ref:`metadata routing developer guide
<sphx_glr_auto_examples_miscellaneous_plot_metadata_routing.py>`.

.. GENERATED FROM PYTHON SOURCE LINES 43-52

HDBSCAN: hierarchical density-based clustering
----------------------------------------------
Originally hosted in the scikit-learn-contrib repository, :class:`cluster.HDBSCAN`
has been adpoted into scikit-learn. It's missing a few features from the original
implementation which will be added in future releases.
By performing a modified version of :class:`cluster.DBSCAN` over multiple epsilon
values simultaneously, :class:`cluster.HDBSCAN` finds clusters of varying densities
making it more robust to parameter selection than :class:`cluster.DBSCAN`.
More details in the :ref:`User Guide <hdbscan>`.

.. GENERATED FROM PYTHON SOURCE LINES 52-66

.. code-block:: Python

    import numpy as np
    from sklearn.cluster import HDBSCAN
    from sklearn.datasets import load_digits
    from sklearn.metrics import v_measure_score

    X, true_labels = load_digits(return_X_y=True)
    print(f"number of digits: {len(np.unique(true_labels))}")

    hdbscan = HDBSCAN(min_cluster_size=15).fit(X)
    non_noisy_labels = hdbscan.labels_[hdbscan.labels_ != -1]
    print(f"number of clusters found: {len(np.unique(non_noisy_labels))}")

    print(v_measure_score(true_labels[hdbscan.labels_ != -1], non_noisy_labels))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    number of digits: 10
    number of clusters found: 10
    0.9751818034688537




.. GENERATED FROM PYTHON SOURCE LINES 67-73

TargetEncoder: a new category encoding strategy
-----------------------------------------------
Well suited for categorical features with high cardinality,
:class:`preprocessing.TargetEncoder` encodes the categories based on a shrunk
estimate of the average target values for observations belonging to that category.
More details in the :ref:`User Guide <target_encoder>`.

.. GENERATED FROM PYTHON SOURCE LINES 73-84

.. code-block:: Python

    import numpy as np
    from sklearn.preprocessing import TargetEncoder

    X = np.array([["cat"] * 30 + ["dog"] * 20 + ["snake"] * 38], dtype=object).T
    y = [90.3] * 30 + [20.4] * 20 + [21.2] * 38

    enc = TargetEncoder(random_state=0)
    X_trans = enc.fit_transform(X, y)

    enc.encodings_





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    [array([90.3, 20.4, 21.2])]



.. GENERATED FROM PYTHON SOURCE LINES 85-92

Missing values support in decision trees
----------------------------------------
The classes :class:`tree.DecisionTreeClassifier` and
:class:`tree.DecisionTreeRegressor` now support missing values. For each potential
threshold on the non-missing data, the splitter will evaluate the split with all the
missing values going to the left node or the right node.
More details in the :ref:`User Guide <tree_missing_value_support>`.

.. GENERATED FROM PYTHON SOURCE LINES 92-101

.. code-block:: Python

    import numpy as np
    from sklearn.tree import DecisionTreeClassifier

    X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
    y = [0, 0, 1, 1]

    tree = DecisionTreeClassifier(random_state=0).fit(X, y)
    tree.predict(X)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    array([0, 0, 1, 1])



.. GENERATED FROM PYTHON SOURCE LINES 102-106

New display `model_selection.ValidationCurveDisplay`
----------------------------------------------------
:class:`model_selection.ValidationCurveDisplay` is now available to plot results
from :func:`model_selection.validation_curve`.

.. GENERATED FROM PYTHON SOURCE LINES 106-122

.. code-block:: Python

    from sklearn.datasets import make_classification
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import ValidationCurveDisplay

    X, y = make_classification(1000, 10, random_state=0)

    _ = ValidationCurveDisplay.from_estimator(
        LogisticRegression(),
        X,
        y,
        param_name="C",
        param_range=np.geomspace(1e-5, 1e3, num=9),
        score_type="both",
        score_name="Accuracy",
    )




.. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_3_0_001.png
   :alt: plot release highlights 1 3 0
   :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_3_0_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 123-128

Gamma loss for gradient boosting
--------------------------------
The class :class:`ensemble.HistGradientBoostingRegressor` supports the
Gamma deviance loss function via `loss="gamma"`. This loss function is useful for
modeling strictly positive targets with a right-skewed distribution.

.. GENERATED FROM PYTHON SOURCE LINES 128-141

.. code-block:: Python

    import numpy as np
    from sklearn.model_selection import cross_val_score
    from sklearn.datasets import make_low_rank_matrix
    from sklearn.ensemble import HistGradientBoostingRegressor

    n_samples, n_features = 500, 10
    rng = np.random.RandomState(0)
    X = make_low_rank_matrix(n_samples, n_features, random_state=rng)
    coef = rng.uniform(low=-10, high=20, size=n_features)
    y = rng.gamma(shape=2, scale=np.exp(X @ coef) / 2)
    gbdt = HistGradientBoostingRegressor(loss="gamma")
    cross_val_score(gbdt, X, y).mean()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    0.46858513287221665



.. GENERATED FROM PYTHON SOURCE LINES 142-149

Grouping infrequent categories in :class:`preprocessing.OrdinalEncoder`
-----------------------------------------------------------------------
Similarly to :class:`preprocessing.OneHotEncoder`, the class
:class:`preprocessing.OrdinalEncoder` now supports aggregating infrequent categories
into a single output for each feature. The parameters to enable the gathering of
infrequent categories are `min_frequency` and `max_categories`.
See the :ref:`User Guide <encoder_infrequent_categories>` for more details.

.. GENERATED FROM PYTHON SOURCE LINES 149-157

.. code-block:: Python

    from sklearn.preprocessing import OrdinalEncoder
    import numpy as np

    X = np.array(
        [["dog"] * 5 + ["cat"] * 20 + ["rabbit"] * 10 + ["snake"] * 3], dtype=object
    ).T
    enc = OrdinalEncoder(min_frequency=6).fit(X)
    enc.infrequent_categories_




.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    [array(['dog', 'snake'], dtype=object)]




.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.474 seconds)


.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_3_0.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_1_3_0.ipynb
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        :alt: Launch JupyterLite
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