.. 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_0_24_0.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_24_0.py: ======================================== 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 `. 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 25-51 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 ` (note: the Successive Halving estimators are still :term:`experimental `). .. figure:: ../model_selection/images/sphx_glr_plot_successive_halving_iterations_001.png :target: ../model_selection/plot_successive_halving_iterations.html :align: center .. GENERATED FROM PYTHON SOURCE LINES 51-79 .. code-block:: Python 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_ .. rst-class:: sphx-glr-script-out .. code-block:: none {'bootstrap': True, 'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 10} .. GENERATED FROM PYTHON SOURCE LINES 80-99 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 `. .. 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 `. .. GENERATED FROM PYTHON SOURCE LINES 101-109 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 `_. .. GENERATED FROM PYTHON SOURCE LINES 111-119 New self-training meta-estimator -------------------------------- A new self-training implementation, based on `Yarowski's algorithm `_ 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 `. .. GENERATED FROM PYTHON SOURCE LINES 119-133 .. code-block:: Python 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) .. raw:: html

SelfTrainingClassifier(estimator=SVC(gamma='auto', probability=True))

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

.. GENERATED FROM PYTHON SOURCE LINES 134-142 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 `. .. GENERATED FROM PYTHON SOURCE LINES 142-157 .. code-block:: Python 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()}" ) .. rst-class:: sphx-glr-script-out .. code-block:: none Features selected by forward sequential selection: ['sepal length (cm)', 'petal width (cm)'] .. GENERATED FROM PYTHON SOURCE LINES 158-164 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`. .. GENERATED FROM PYTHON SOURCE LINES 164-183 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none 0.7357 .. GENERATED FROM PYTHON SOURCE LINES 184-185 For comparison, here is the score of a linear baseline for the same data: .. GENERATED FROM PYTHON SOURCE LINES 185-189 .. code-block:: Python linear_baseline = make_pipeline(MinMaxScaler(), LogisticRegression(max_iter=1000)) linear_baseline.fit(X_train, y_train).score(X_test, y_test) .. rst-class:: sphx-glr-script-out .. code-block:: none 0.7138 .. GENERATED FROM PYTHON SOURCE LINES 190-196 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 ` .. GENERATED FROM PYTHON SOURCE LINES 196-227 .. code-block:: Python from sklearn.ensemble import RandomForestRegressor from sklearn.datasets import fetch_california_housing # from sklearn.inspection import plot_partial_dependence from sklearn.inspection import PartialDependenceDisplay 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) # plot_partial_dependence has been removed in version 1.2. From 1.2, use # PartialDependenceDisplay instead. # display = plot_partial_dependence( display = PartialDependenceDisplay.from_estimator( 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) .. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_0_24_0_001.png :alt: Partial dependence of house value on non-location features for the California housing dataset, with BayesianRidge :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_0_24_0_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 228-234 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. .. GENERATED FROM PYTHON SOURCE LINES 234-248 .. code-block:: Python 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) .. raw:: html

DecisionTreeRegressor(criterion='poisson', random_state=0)

.. GENERATED FROM PYTHON SOURCE LINES 249-265 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 `, - an example illustrating how to :ref:`statistically compare the performance of models ` evaluated using :class:`~sklearn.model_selection.GridSearchCV`, - an example on how to :ref:`interpret coefficients of linear models `, - an :ref:`example ` comparing Principal Component Regression and Partial Least Squares. .. rst-class:: sphx-glr-timing **Total running time of the script:** (1 minutes 9.722 seconds) .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_24_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_0_24_0.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_release_highlights_0_24_0.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_release_highlights_0_24_0.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_release_highlights_0_24_0.zip ` .. include:: plot_release_highlights_0_24_0.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_