franslarsson
diff --git a/‎doc/glossary.rst
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diff --git a/‎doc/modules/partial_dependence.rst
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diff --git a/‎examples/release_highlights/plot_release_highlights_0_24_0.py
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@@ -328,6 +328,12 @@ General Concepts
         * sometimes in the :ref:`User Guide <user_guide>` (built from ``doc/``)
 
+    experimental
+        An experimental tool is already usable but its public API, such as
+        default parameter values or fitted attributes, is still subject to
+        change in future versions without the usual :term:`deprecation`
+        warning policy.
+
     evaluation metric
     evaluation metrics
         Evaluation metrics give a measure of how well a model performs.  We may
 
@@ -105,6 +105,8 @@ generated. The ``values`` field returned by
 used in the grid for each input feature of interest. They also correspond to
 the axis of the plots.
 
+.. _individual_conditional:
+
 Individual conditional expectation (ICE) plot
 =============================================
 
@@ -180,7 +182,7 @@ values are defined by :math:`x_S` for the features in :math:`X_S`, and by
 
 Computing this integral for various values of :math:`x_S` produces a PDP plot
 as above. An ICE line is defined as a single :math:`f(x_{S}, x_{C}^{(i)})`
-evaluated at at :math:`x_{S}`.
+evaluated at :math:`x_{S}`.
 
 Computation methods
 ===================
 
@@ -0,0 +1,245 @@
+# flake8: noqa
+"""
+========================================
+Release Highlights for scikit-learn 0.24
+========================================
+
+.. currentmodule:: sklearn
+
+We are pleased to announce the release of scikit-learn 0.24! 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 <changes_0_24>`.
+
+To install the latest version (with pip)::
+
+    pip install --upgrade scikit-learn
+
+or with conda::
+
+    conda install -c conda-forge scikit-learn
+"""
+
+##############################################################################
+# Successive Halving estimators for tuning hyper-parameters
+# ---------------------------------------------------------
+# Successive Halving, a state of the art method, is now available to
+# explore the space of the parameters and identify their best combination.
+# :class:`~sklearn.model_selection.HalvingGridSearchCV` and
+# :class:`~sklearn.model_selection.HalvingRandomSearchCV` can be
+# used as drop-in replacement for
+# :class:`~sklearn.model_selection.GridSearchCV` and
+# :class:`~sklearn.model_selection.RandomizedSearchCV`.
+# Successive Halving is an iterative selection process illustrated in the
+# figure below. The first iteration is run with a small amount of resources,
+# where the resource typically corresponds to the number of training samples,
+# but can also be an arbitrary integer parameter such as `n_estimators` in a
+# random forest. Only a subset of the parameter candidates are selected for the
+# next iteration, which will be run with an increasing amount of allocated
+# resources. Only a subset of candidates will last until the end of the
+# iteration process, and the best parameter candidate is the one that has the
+# highest score on the last iteration.
+#
+# Read more in the :ref:`User Guide <successive_halving_user_guide>` (note:
+# the Successive Halving estimators are still :term:`experimental
+# <experimental>`).
+# 
+# .. figure:: ../model_selection/images/sphx_glr_plot_successive_halving_iterations_001.png
+#   :target: ../model_selection/plot_successive_halving_iterations.html
+#   :align: center
+
+import numpy as np
+from scipy.stats import randint
+from sklearn.experimental import enable_halving_search_cv  # noqa
+from sklearn.model_selection import HalvingRandomSearchCV
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.datasets import make_classification
+
+rng = np.random.RandomState(0)
+
+X, y = make_classification(n_samples=700, random_state=rng)
+
+clf = RandomForestClassifier(n_estimators=10, random_state=rng)
+
+param_dist = {"max_depth": [3, None],
+              "max_features": randint(1, 11),
+              "min_samples_split": randint(2, 11),
+              "bootstrap": [True, False],
+              "criterion": ["gini", "entropy"]}
+
+rsh = HalvingRandomSearchCV(estimator=clf, param_distributions=param_dist,
+                            factor=2, random_state=rng)
+rsh.fit(X, y)
+rsh.best_params_
+
+##############################################################################
+# Native support for categorical features in HistGradientBoosting estimators
+# --------------------------------------------------------------------------
+# :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and
+# :class:`~sklearn.ensemble.HistGradientBoostingRegressor` now have native
+# support for categorical features: they can consider splits on non-ordered,
+# categorical data. Read more in the :ref:`User Guide
+# <categorical_support_gbdt>`.
+#
+# .. figure:: ../ensemble/images/sphx_glr_plot_gradient_boosting_categorical_001.png
+#   :target: ../ensemble/plot_gradient_boosting_categorical.html
+#   :align: center
+#
+# The plot shows that the new native support for categorical features leads to
+# fitting times that are comparable to models where the categories are treated
+# as ordered quantities, i.e. simply ordinal-encoded. Native support is also
+# more expressive than both one-hot encoding and ordinal encoding. However, to
+# use the new `categorical_features` parameter, it is still required to
+# preprocess the data within a pipeline as demonstrated in this :ref:`example
+# <sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py>`.
+
+##############################################################################
+# Improved performances of HistGradientBoosting estimators
+# --------------------------------------------------------
+# The memory footprint of :class:`ensemble.HistGradientBoostingRegressor` and
+# :class:`ensemble.HistGradientBoostingClassifier` has been significantly
+# improved during calls to `fit`. In addition, histogram initialization is now
+# done in parallel which results in slight speed improvements.
+# See more in the `Benchmark page
+# <https://scikit-learn.org/scikit-learn-benchmarks/>`_.
+
+##############################################################################
+# New self-training meta-estimator
+# --------------------------------
+# A new self-training implementation, based on `Yarowski's algorithm
+# <https://doi.org/10.3115/981658.981684>`_ can now be used with any
+# classifier that implements :term:`predict_proba`. The sub-classifier
+# will behave as a
+# semi-supervised classifier, allowing it to learn from unlabeled data.
+# Read more in the :ref:`User guide <self_training>`.
+
+import numpy as np
+from sklearn import datasets
+from sklearn.semi_supervised import SelfTrainingClassifier
+from sklearn.svm import SVC
+
+rng = np.random.RandomState(42)
+iris = datasets.load_iris()
+random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3
+iris.target[random_unlabeled_points] = -1
+svc = SVC(probability=True, gamma="auto")
+self_training_model = SelfTrainingClassifier(svc)
+self_training_model.fit(iris.data, iris.target)
+
+##############################################################################
+# New SequentialFeatureSelector transformer
+# -----------------------------------------
+# A new iterative transformer to select features is available:
+# :class:`~sklearn.feature_selection.SequentialFeatureSelector`.
+# Sequential Feature Selection can add features one at a time (forward
+# selection) or remove features from the list of the available features
+# (backward selection), based on a cross-validated score maximization.
+# See the :ref:`User Guide <sequential_feature_selection>`.
+
+from sklearn.feature_selection import SequentialFeatureSelector
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.datasets import load_iris
+
+X, y = load_iris(return_X_y=True, as_frame=True)
+feature_names = X.columns
+knn = KNeighborsClassifier(n_neighbors=3)
+sfs = SequentialFeatureSelector(knn, n_features_to_select=2)
+sfs.fit(X, y)
+print("Features selected by forward sequential selection: "
+      f"{feature_names[sfs.get_support().tolist()]}")
+
+##############################################################################
+# New PolynomialCountSketch kernel approximation function
+# -------------------------------------------------------
+# The new :class:`~sklearn.kernel_approximation.PolynomialCountSketch`
+# approximates a polynomial expansion of a feature space when used with linear
+# models, but uses much less memory than
+# :class:`~sklearn.preprocessing.PolynomialFeatures`.
+
+from sklearn.datasets import fetch_covtype
+from sklearn.pipeline import make_pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.kernel_approximation import PolynomialCountSketch
+from sklearn.linear_model import LogisticRegression
+
+X, y = fetch_covtype(return_X_y=True)
+pipe = make_pipeline(MinMaxScaler(),
+                     PolynomialCountSketch(degree=2, n_components=300),
+                     LogisticRegression(max_iter=1000))
+X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=5000,
+                                                    test_size=10000,
+                                                    random_state=42)
+pipe.fit(X_train, y_train).score(X_test, y_test)
+
+# ##############################################################################
+# # For comparison, here is the score of a linear baseline for the same data:
+
+linear_baseline = make_pipeline(MinMaxScaler(),
+                                LogisticRegression(max_iter=1000))
+linear_baseline.fit(X_train, y_train).score(X_test, y_test)
+
+##############################################################################
+# Individual Conditional Expectation plots
+# ----------------------------------------
+# A new kind of partial dependence plot is available: the Individual
+# Conditional Expectation (ICE) plot. ICE plots visualize the dependence of the
+# prediction on a feature for each sample separately, with one line per sample.
+# See the :ref:`User Guide <individual_conditional>`
+
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.datasets import fetch_california_housing
+from sklearn.inspection import plot_partial_dependence
+
+X, y = fetch_california_housing(return_X_y=True, as_frame=True)
+features = ['MedInc', 'AveOccup', 'HouseAge', 'AveRooms']
+est = RandomForestRegressor(n_estimators=10)
+est.fit(X, y)
+display = plot_partial_dependence(
+       est, X, features, kind="individual", subsample=50,
+       n_jobs=3, grid_resolution=20, random_state=0
+)
+display.figure_.suptitle(
+    'Partial dependence of house value on non-location features\n'
+    'for the California housing dataset, with BayesianRidge'
+)
+display.figure_.subplots_adjust(hspace=0.3)
+
+##############################################################################
+# New Poisson splitting criterion for DecisionTreeRegressor
+# ---------------------------------------------------------
+# The integration of Poisson regression estimation continues from version 0.23.
+# :class:`~sklearn.tree.DecisionTreeRegressor` now supports a new `'poisson'`
+# splitting criterion. Setting `criterion="poisson"` might be a good choice
+# if your target is a count or a frequency.
+
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.model_selection import train_test_split
+import numpy as np
+
+n_samples, n_features = 1000, 20
+rng = np.random.RandomState(0)
+X = rng.randn(n_samples, n_features)
+# positive integer target correlated with X[:, 5] with many zeros:
+y = rng.poisson(lam=np.exp(X[:, 5]) / 2)
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
+regressor = DecisionTreeRegressor(criterion='poisson', random_state=0)
+regressor.fit(X_train, y_train)
+
+##############################################################################
+# New documentation improvements
+# ------------------------------
+#
+# New examples and documentation pages have been added, in a continuous effort
+# to improve the understanding of machine learning practices:
+#
+# - a new section about :ref:`common pitfalls and recommended
+#   practices <common_pitfalls>`,
+# - an example illustrating how to :ref:`statistically compare the performance of
+#   models <sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py>`
+#   evaluated using :class:`~sklearn.model_selection.GridSearchCV`,
+# - an example on how to :ref:`interpret coefficients of linear models
+#   <sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py>`,
+# - an :ref:`example
+#   <sphx_glr_auto_examples_cross_decomposition_plot_pcr_vs_pls.py>`
+#   comparing Principal Component Regression and Partial Least Squares.