sklearn.base¶

• API Change From version 0.24 base.BaseEstimator.get_params will raise an AttributeError rather than return None for parameters that are in the estimator’s constructor but not stored as attributes on the instance. #14464 by Joel Nothman.

sklearn.calibration¶

• Fix Fixed a bug that made calibration.CalibratedClassifierCV fail when given a sample_weight parameter of type list (in the case where sample_weights are not supported by the wrapped estimator). #13575 by William de Vazelhes.

sklearn.cluster¶

• Feature cluster.SpectralClustering now accepts precomputed sparse neighbors graph as input. #10482 by Tom Dupre la Tour and Kumar Ashutosh.

• Enhancement cluster.SpectralClustering now accepts a n_components parameter. This parameter extends SpectralClustering class functionality to match cluster.spectral_clustering. #13726 by Shuzhe Xiao.

• Fix Fixed a bug where cluster.KMeans produced inconsistent results between n_jobs=1 and n_jobs>1 due to the handling of the random state. #9288 by Bryan Yang.

sklearn.compose¶

• Fix Fixed a bug in compose.ColumnTransformer which failed to select the proper columns when using a boolean list, with NumPy older than 1.12. #14510 by Guillaume Lemaitre.

• Fix Fixed a bug in compose.TransformedTargetRegressor which did not pass **fit_params to the underlying regressor. #14890 by Miguel Cabrera.

sklearn.cross_decomposition¶

• Fix Fixed a bug where cross_decomposition.PLSCanonical and cross_decomposition.PLSRegression were raising an error when fitted with a target matrix Y in which the first column was constant. #13609 by Camila Williamson.

sklearn.datasets¶

• Feature datasets.fetch_openml now supports heterogeneous data using pandas by setting as_frame=True. #13902 by Thomas Fan.

• Enhancement The parameter return_X_y was added to datasets.fetch_20newsgroups and datasets.fetch_olivetti_faces . #14259 by Sourav Singh.

• Enhancement datasets.make_classification now accepts array-like weights parameter, i.e. list or numpy.array, instead of list only. #14764 by Cat Chenal.

• Fix Fixed a bug in datasets.fetch_openml, which failed to load an OpenML dataset that contains an ignored feature. #14623 by Sarra Habchi.

sklearn.decomposition¶

• Enhancement decomposition.dict_learning and decomposition.dict_learning_online now accept method_max_iter and pass it to decomposition.sparse_encode. #12650 by Adrin Jalali.

• Enhancement decomposition.SparseCoder, decomposition.DictionaryLearning, and decomposition.MiniBatchDictionaryLearning now take a transform_max_iter parameter and pass it to either decomposition.dict_learning or decomposition.sparse_encode. #12650 by Adrin Jalali.

• Enhancement decomposition.IncrementalPCA now accepts sparse matrices as input, converting them to dense in batches thereby avoiding the need to store the entire dense matrix at once. #13960 by Scott Gigante.

• Fix decomposition.sparse_encode now passes the max_iter to the underlying linear_model.LassoLars when algorithm='lasso_lars'. #12650 by Adrin Jalali.

sklearn.dummy¶

• Fix dummy.DummyClassifier now handles checking the existence of the provided constant in multiouput cases. #14908 by Martina G. Vilas.

• API Change The outputs_2d_ attribute is deprecated in dummy.DummyClassifier and dummy.DummyRegressor. It is equivalent to n_outputs > 1. #14933 by Nicolas Hug

sklearn.ensemble¶

• Major Feature Added ensemble.StackingClassifier and ensemble.StackingRegressor to stack predictors using a final classifier or regressor. #11047 by Guillaume Lemaitre and Caio Oliveira.

• Many improvements were made to ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor:

  • Major Feature Estimators now natively support dense data with missing values both for training and predicting. They also support infinite values. #13911 and #14406 by Nicolas Hug, Adrin Jalali and Olivier Grisel.

  • Feature Estimators now have an additional warm_start parameter that enables warm starting. #14012 by Johann Faouzi.

  • Enhancement for ensemble.HistGradientBoostingClassifier the training loss or score is now monitored on a class-wise stratified subsample to preserve the class balance of the original training set. #14194 by Johann Faouzi.

  • Feature inspection.partial_dependence and inspection.plot_partial_dependence now support the fast ‘recursion’ method for both estimators. #13769 by Nicolas Hug.

  • Enhancement ensemble.HistGradientBoostingRegressor now supports the ‘least_absolute_deviation’ loss. #13896 by Nicolas Hug.

  • Fix Estimators now bin the training and validation data separately to avoid any data leak. #13933 by Nicolas Hug.

  • Fix Fixed a bug where early stopping would break with string targets. #14710 by Guillaume Lemaitre.

  • Fix ensemble.HistGradientBoostingClassifier now raises an error if categorical_crossentropy loss is given for a binary classification problem. #14869 by Adrin Jalali.

  Note that pickles from 0.21 will not work in 0.22.

• Fix ensemble.VotingClassifier.predict_proba will no longer be present when voting='hard'. #14287 by Thomas Fan.

• Fix Run by default utils.estimator_checks.check_estimator on both ensemble.VotingClassifier and ensemble.VotingRegressor. It leads to solve issues regarding shape consistency during predict which was failing when the underlying estimators were not outputting consistent array dimensions. Note that it should be replaced by refactoring the common tests in the future. #14305 by Guillaume Lemaitre.

• Fix ensemble.AdaBoostClassifier computes probabilities based on the decision function as in the literature. Thus, predict and predict_proba give consistent results. #14114 by Guillaume Lemaitre.

• API Change presort is now deprecated in ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor, and the parameter has no effect. Users are recommended to use ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor instead. #14907 by Adrin Jalali.

• Enhancement Addition of max_samples argument allows limiting size of bootstrap samples to be less than size of dataset. Added to ensemble.ForestClassifier, ensemble.ForestRegressor, ensemble.RandomForestClassifier, ensemble.RandomForestRegressor, ensemble.ExtraTreesClassifier, ensemble.ExtraTreesRegressor, ensemble.RandomTreesEmbedding. #14682 by Matt Hancock and #5963 by Pablo Duboue.

sklearn.feature_extraction¶

• Enhancement A warning will now be raised if a parameter choice means that another parameter will be unused on calling the fit() method for feature_extraction.text.HashingVectorizer, feature_extraction.text.CountVectorizer and feature_extraction.text.TfidfVectorizer. #14602 by Gaurav Chawla.

• Fix Functions created by build_preprocessor and build_analyzer of feature_extraction.text.VectorizerMixin can now be pickled. #14430 by Dillon Niederhut.

• API Change Deprecated unused copy param for feature_extraction.text.TfidfVectorizer.transform it will be removed in v0.24. #14520 by Guillem G. Subies.

sklearn.feature_selection¶

• Fix Fixed a bug where feature_selection.VarianceThreshold with threshold=0 did not remove constant features due to numerical instability, by using range rather than variance in this case. #13704 by Roddy MacSween.

sklearn.gaussian_process¶

• Feature gaussian_process.GaussianProcessClassifier.log_marginal_likelihood and gaussian_process.GaussianProcessRegressor.log_marginal_likelihood now accept a clone_kernel=True keyword argument. When set to False, the kernel attribute is modified, but may result in a performance improvement. #14378 by Masashi Shibata.

• API Change From version 0.24 gaussian_process.kernels.Kernel.get_params will raise an AttributeError rather than return None for parameters that are in the estimator’s constructor but not stored as attributes on the instance. #14464 by Joel Nothman.

sklearn.impute¶

• Major Feature Added impute.KNNImputer, to impute missing values using k-Nearest Neighbors. #12852 by Ashim Bhattarai and Thomas Fan.

• Feature impute.IterativeImputer has new skip_compute flag that is False by default, which, when True, will skip computation on features that have no missing values during the fit phase. #13773 by Sergey Feldman.

• Fix impute.IterativeImputer now works when there is only one feature. By Sergey Feldman.

• Efficiency impute.MissingIndicator.fit_transform avoid repeated computation of the masked matrix. #14356 by Harsh Soni.

sklearn.inspection¶

• Major Feature inspection.permutation_importance has been added to measure the importance of each feature in an arbitrary trained model with respect to a given scoring function. #13146 by Thomas Fan.

• Feature inspection.partial_dependence and inspection.plot_partial_dependence now support the fast ‘recursion’ method for ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor. #13769 by Nicolas Hug.

sklearn.kernel_approximation¶

• Fix Fixed a bug where kernel_approximation.Nystroem raised a KeyError when using kernel="precomputed". #14706 by Venkatachalam N.

sklearn.linear_model¶

• Enhancement linear_model.BayesianRidge now accepts hyperparameters alpha_init and lambda_init which can be used to set the initial value of the maximization procedure in fit. #13618 by Yoshihiro Uchida.

• Efficiency The ‘liblinear’ logistic regression solver is now faster and requires less memory. #14108, pr:14170, pr:14296 by Alex Henrie.

• Fix linear_model.Ridge now correctly fits an intercept when X is sparse, solver="auto" and fit_intercept=True, because the default solver in this configuration has changed to sparse_cg, which can fit an intercept with sparse data. #13995 by Jérôme Dockès.

• Fix linear_model.Ridge with solver='sag' now accepts F-ordered and non-contiguous arrays and makes a conversion instead of failing. #14458 by Guillaume Lemaitre.

• Fix linear_model.LassoCV no longer forces precompute=False when fitting the final model. #14591 by Andreas Müller.

• Fix linear_model.RidgeCV and linear_model.RidgeClassifierCV now correctly scores when cv=None. #14864 by Venkatachalam N.

sklearn.manifold¶

• Feature manifold.Isomap, manifold.TSNE, and manifold.SpectralEmbedding now accept precomputed sparse neighbors graph as input. #10482 by Tom Dupre la Tour and Kumar Ashutosh.

• API Change Deprecate training_data_ unused attribute in manifold.Isomap. #10482 by Tom Dupre la Tour.

• Fix Fixed a bug where manifold.spectral_embedding (and therefore manifold.SpectralEmbedding and cluster.SpectralClustering) computed wrong eigenvalues with eigen_solver='amg' when n_samples < 5 * n_components. #14647 by Andreas Müller.

• Fix Fixed a bug in manifold.spectral_embedding used in manifold.SpectralEmbedding and cluster.SpectralClustering where eigen_solver="amg" would sometimes result in a LinAlgError. #13393 by Andrew Knyazev #13707 by Scott White

sklearn.metrics¶

• Major Feature metrics.plot_roc_curve has been added to plot roc curves. This function introduces the visualization API described in the User Guide. #14357 by Thomas Fan.

• Feature Added the metrics.pairwise.nan_euclidean_distances metric, which calculates euclidean distances in the presence of missing values. #12852 by Ashim Bhattarai and Thomas Fan.

• Feature New ranking metrics metrics.ndcg_score and metrics.dcg_score have been added to compute Discounted Cumulative Gain and Normalized Discounted Cumulative Gain. #9951 by Jérôme Dockès.

• Feature Added multiclass support to metrics.roc_auc_score. #12789 by Kathy Chen, Mohamed Maskani, and Thomas Fan.

• Feature Add metrics.mean_tweedie_deviance measuring the Tweedie deviance for a power parameter p. Also add mean Poisson deviance metrics.mean_poisson_deviance and mean Gamma deviance metrics.mean_gamma_deviance that are special cases of the Tweedie deviance for p=1 and p=2 respectively. #13938 by Christian Lorentzen and Roman Yurchak.

• Enhancement The parameter beta in metrics.fbeta_score is updated to accept the zero and float('+inf') value. #13231 by Dong-hee Na.

• Enhancement Added parameter squared in metrics.mean_squared_error to return root mean squared error. #13467 by Urvang Patel.

• Enhancement Allow computing averaged metrics in the case of no true positives. #14595 by Andreas Müller.

• Fix Raise a ValueError in metrics.silhouette_score when a precomputed distance matrix contains non-zero diagonal entries. #12258 by Stephen Tierney.

• API Change scoring="neg_brier_score" should be used instead of scoring="brier_score_loss" which is now deprecated. #14898 by Stefan Matcovici.

sklearn.model_selection¶

• Efficiency Improved performance of multimetric scoring in model_selection.cross_validate, model_selection.GridSearchCV, and model_selection.RandomizedSearchCV. #14593 by Thomas Fan.

• Enhancement model_selection.learning_curve now accepts parameter return_times which can be used to retrieve computation times in order to plot model scalability (see learning_curve example). #13938 by Hadrien Reboul.

• Enhancement model_selection.RandomizedSearchCV now accepts lists of parameter distributions. #14549 by Andreas Müller.

• Fix Reimplemented model_selection.StratifiedKFold to fix an issue where one test set could be n_classes larger than another. Test sets should now be near-equally sized. #14704 by Joel Nothman.

sklearn.multioutput¶

• Fix multioutput.MultiOutputClassifier now has attribute classes_. #14629 by Agamemnon Krasoulis.

sklearn.naive_bayes¶

• Major Feature Added naive_bayes.CategoricalNB that implements the Categorical Naive Bayes classifier. #12569 by Tim Bicker and Florian Wilhelm.

sklearn.neighbors¶

• Major Feature Added neighbors.KNeighborsTransformer and neighbors.RadiusNeighborsTransformer, which transform input dataset into a sparse neighbors graph. They give finer control on nearest neighbors computations and enable easy pipeline caching for multiple use. #10482 by Tom Dupre la Tour.

• Feature neighbors.KNeighborsClassifier, neighbors.KNeighborsRegressor, neighbors.RadiusNeighborsClassifier, neighbors.RadiusNeighborsRegressor, and neighbors.LocalOutlierFactor now accept precomputed sparse neighbors graph as input. #10482 by Tom Dupre la Tour and Kumar Ashutosh.

• Feature neighbors.RadiusNeighborsClassifier now supports predicting probabilities by using predict_proba and supports more outlier_label options: ‘most_frequent’, or different outlier_labels for multi-outputs. #9597 by Wenbo Zhao.

• Efficiency Efficiency improvements for neighbors.RadiusNeighborsClassifier.predict. #9597 by Wenbo Zhao.

• Fix neighbors.KNeighborsRegressor now throws error when metric='precomputed' and fit on non-square data. #14336 by Gregory Dexter.

sklearn.neural_network¶

• Feature Add max_fun parameter in neural_network.BaseMultilayerPerceptron, neural_network.MLPRegressor, and neural_network.MLPClassifier to give control over maximum number of function evaluation to not meet tol improvement. #9274 by Daniel Perry.

sklearn.pipeline¶

• Enhancement pipeline.Pipeline now supports score_samples if the final estimator does. #13806 by Anaël Beaugnon.

sklearn.preprocessing¶

• Enhancement Avoid unnecessary data copy when fitting preprocessors preprocessing.StandardScaler, preprocessing.MinMaxScaler, preprocessing.MaxAbsScaler, preprocessing.RobustScaler and preprocessing.QuantileTransformer which results in a slight performance improvement. #13987 by Roman Yurchak.

• Fix KernelCenterer now throws error when fit on non-square preprocessing.KernelCenterer #14336 by Gregory Dexter.

sklearn.svm¶

• Enhancement svm.SVC and svm.NuSVC now accept a break_ties parameter. This parameter results in predict breaking the ties according to the confidence values of decision_function, if decision_function_shape='ovr', and the number of target classes > 2. #12557 by Adrin Jalali.

• Enhancement SVM estimators now throw a more specific error when kernel='precomputed' and fit on non-square data. #14336 by Gregory Dexter.

sklearn.tree¶

• Feature Adds minimal cost complexity pruning, controlled by ccp_alpha, to tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier, tree.ExtraTreeRegressor, ensemble.RandomForestClassifier, ensemble.RandomForestRegressor, ensemble.ExtraTreesClassifier, ensemble.ExtraTreesRegressor, ensemble.RandomTreesEmbedding, ensemble.GradientBoostingClassifier, and ensemble.GradientBoostingRegressor. #12887 by Thomas Fan.

• API Change presort is now deprecated in tree.DecisionTreeClassifier and tree.DecisionTreeRegressor, and the parameter has no effect. #14907 by Adrin Jalali.

• API Change The classes_ and n_classes_ attributes of tree.DecisionTreeRegressor are now deprecated. #15028 by Mei Guan, Nicolas Hug, and Adrin Jalali.

sklearn.utils¶

• Feature check_estimator can now generate checks by setting generate_only=True. Previously, running check_estimator will stop when the first check fails. With generate_only=True, all checks can run independently and report the ones that are failing. Read more in Rolling your own estimator. #14381 by Thomas Fan.

• Feature Added a pytest specific decorator, parametrize_with_checks, to parametrize estimator checks for a list of estimators. #14381 by Thomas Fan.

• API Change The following utils have been deprecated and are now private: - choose_check_classifiers_labels - enforce_estimator_tags_y

• Enhancement utils.safe_indexing accepts an axis parameter to index array-like across rows and columns. The column indexing can be done on NumPy array, SciPy sparse matrix, and Pandas DataFrame. An additional refactoring was done. #14035 and #14475 by Guillaume Lemaitre.

• Enhancement utils.extmath.safe_sparse_dot works between 3D+ ndarray and sparse matrix. #14538 by Jérémie du Boisberranger.

• Fix utils.check_array is now raising an error instead of casting NaN to integer. #14872 by Roman Yurchak.

Miscellaneous¶

• API Change Replace manual checks with check_is_fitted. Errors thrown when using a non-fitted estimators are now more uniform. #13013 by Agamemnon Krasoulis.

• Fix Port lobpcg from SciPy which implement some bug fixes but only available in 1.3+. #13609 by Guillaume Lemaitre.

sklearn.cluster¶

• Fix Fixed a bug in cluster.KMeans where computation with init='random' was single threaded for n_jobs > 1 or n_jobs = -1. #12955 by Prabakaran Kumaresshan.

• Fix Fixed a bug in cluster.OPTICS where users were unable to pass float min_samples and min_cluster_size. #14496 by Fabian Klopfer and Hanmin Qin.

• Fix Fixed a bug in cluster.KMeans where KMeans++ initialisation could rarely result in an IndexError. #11756 by Joel Nothman.

sklearn.compose¶

• Fix Fixed an issue in compose.ColumnTransformer where using DataFrames whose column order differs between :func:fit and :func:transform could lead to silently passing incorrect columns to the remainder transformer. #14237 by Andreas Schuderer <schuderer>.

sklearn.datasets¶

• Fix datasets.fetch_california_housing, datasets.fetch_covtype, datasets.fetch_kddcup99, datasets.fetch_olivetti_faces, datasets.fetch_rcv1, and datasets.fetch_species_distributions try to persist the previously cache using the new joblib if the cached data was persisted using the deprecated sklearn.externals.joblib. This behavior is set to be deprecated and removed in v0.23. #14197 by Adrin Jalali.

sklearn.ensemble¶

• Fix Fix zero division error in HistGradientBoostingClassifier and HistGradientBoostingRegressor. #14024 by Nicolas Hug <NicolasHug>.

sklearn.impute¶

• Fix Fixed a bug in impute.SimpleImputer and impute.IterativeImputer so that no errors are thrown when there are missing values in training data. #13974 by Frank Hoang <fhoang7>.

sklearn.inspection¶

• Fix Fixed a bug in inspection.plot_partial_dependence where target parameter was not being taken into account for multiclass problems. #14393 by Guillem G. Subies.

sklearn.linear_model¶

• Fix Fixed a bug in linear_model.LogisticRegressionCV where refit=False would fail depending on the 'multiclass' and 'penalty' parameters (regression introduced in 0.21). #14087 by Nicolas Hug.

• Fix Compatibility fix for linear_model.ARDRegression and Scipy>=1.3.0. Adapts to upstream changes to the default pinvh cutoff threshold which otherwise results in poor accuracy in some cases. #14067 by Tim Staley.

sklearn.neighbors¶

• Fix Fixed a bug in neighbors.NeighborhoodComponentsAnalysis where the validation of initial parameters n_components, max_iter and tol required too strict types. #14092 by Jérémie du Boisberranger.

sklearn.tree¶

• Fix Fixed bug in tree.export_text when the tree has one feature and a single feature name is passed in. #14053 by Thomas Fan.

• Fix Fixed an issue with plot_tree where it displayed entropy calculations even for gini criterion in DecisionTreeClassifiers. #13947 by Frank Hoang.

sklearn.decomposition¶

• Fix Fixed a bug in cross_decomposition.CCA improving numerical stability when Y is close to zero. #13903 by Thomas Fan.

sklearn.metrics¶

• Fix Fixed a bug in metrics.pairwise.euclidean_distances where a part of the distance matrix was left un-instanciated for suffiently large float32 datasets (regression introduced in 0.21). #13910 by Jérémie du Boisberranger.

sklearn.preprocessing¶

• Fix Fixed a bug in preprocessing.OneHotEncoder where the new drop parameter was not reflected in get_feature_names. #13894 by James Myatt.

sklearn.utils.sparsefuncs¶

• Fix Fixed a bug where min_max_axis would fail on 32-bit systems for certain large inputs. This affects preprocessing.MaxAbsScaler, preprocessing.normalize and preprocessing.LabelBinarizer. #13741 by Roddy MacSween.

sklearn.inspection¶

• Fix Fixed a bug in inspection.partial_dependence to only check classifier and not regressor for the multiclass-multioutput case. #14309 by Guillaume Lemaitre.

sklearn.metrics¶

• Fix Fixed a bug in metrics.pairwise_distances where it would raise AttributeError for boolean metrics when X had a boolean dtype and Y == None. #13864 by Paresh Mathur.

• Fix Fixed two bugs in metrics.pairwise_distances when n_jobs > 1. First it used to return a distance matrix with same dtype as input, even for integer dtype. Then the diagonal was not zeros for euclidean metric when Y is X. #13877 by Jérémie du Boisberranger.

sklearn.neighbors¶

• Fix Fixed a bug in neighbors.KernelDensity which could not be restored from a pickle if sample_weight had been used. #13772 by Aditya Vyas.

sklearn.base¶

• API Change The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with metrics.r2_score. This will influence the score method of all the multioutput regressors (except for multioutput.MultiOutputRegressor). #13157 by Hanmin Qin.

sklearn.calibration¶

• Enhancement Added support to bin the data passed into calibration.calibration_curve by quantiles instead of uniformly between 0 and 1. #13086 by Scott Cole.

• Enhancement Allow n-dimensional arrays as input for calibration.CalibratedClassifierCV. #13485 by William de Vazelhes.

sklearn.cluster¶

• Major Feature A new clustering algorithm: cluster.OPTICS: an algoritm related to cluster.DBSCAN, that has hyperparameters easier to set and that scales better, by Shane, Adrin Jalali, Erich Schubert, Hanmin Qin, and Assia Benbihi.

• Fix Fixed a bug where cluster.Birch could occasionally raise an AttributeError. #13651 by Joel Nothman.

• Fix Fixed a bug in cluster.KMeans where empty clusters weren’t correctly relocated when using sample weights. #13486 by Jérémie du Boisberranger.

• API Change The n_components_ attribute in cluster.AgglomerativeClustering and cluster.FeatureAgglomeration has been renamed to n_connected_components_. #13427 by Stephane Couvreur.

• Enhancement cluster.AgglomerativeClustering and cluster.FeatureAgglomeration now accept a distance_threshold parameter which can be used to find the clusters instead of n_clusters. #9069 by Vathsala Achar and Adrin Jalali.

sklearn.compose¶

• API Change compose.ColumnTransformer is no longer an experimental feature. #13835 by Hanmin Qin.

sklearn.datasets¶

• Fix Added support for 64-bit group IDs and pointers in SVMLight files. #10727 by Bryan K Woods.

• Fix datasets.load_sample_images returns images with a deterministic order. #13250 by Thomas Fan.

sklearn.decomposition¶

• Enhancement decomposition.KernelPCA now has deterministic output (resolved sign ambiguity in eigenvalue decomposition of the kernel matrix). #13241 by Aurélien Bellet.

• Fix Fixed a bug in decomposition.KernelPCA, fit().transform() now produces the correct output (the same as fit_transform()) in case of non-removed zero eigenvalues (remove_zero_eig=False). fit_inverse_transform was also accelerated by using the same trick as fit_transform to compute the transform of X. #12143 by Sylvain Marié

• Fix Fixed a bug in decomposition.NMF where init = 'nndsvd', init = 'nndsvda', and init = 'nndsvdar' are allowed when n_components < n_features instead of n_components <= min(n_samples, n_features). #11650 by Hossein Pourbozorg and Zijie (ZJ) Poh.

• API Change The default value of the init argument in decomposition.non_negative_factorization will change from random to None in version 0.23 to make it consistent with decomposition.NMF. A FutureWarning is raised when the default value is used. #12988 by Zijie (ZJ) Poh.

sklearn.discriminant_analysis¶

• Enhancement discriminant_analysis.LinearDiscriminantAnalysis now preserves float32 and float64 dtypes. #8769 and #11000 by Thibault Sejourne

• Fix A ChangedBehaviourWarning is now raised when discriminant_analysis.LinearDiscriminantAnalysis is given as parameter n_components > min(n_features, n_classes - 1), and n_components is changed to min(n_features, n_classes - 1) if so. Previously the change was made, but silently. #11526 by William de Vazelhes.

• Fix Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis where the predicted probabilities would be incorrectly computed in the multiclass case. #6848, by Agamemnon Krasoulis and Guillaume Lemaitre <glemaitre>.

• Fix Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis where the predicted probabilities would be incorrectly computed with eigen solver. #11727, by Agamemnon Krasoulis.

sklearn.dummy¶

• Fix Fixed a bug in dummy.DummyClassifier where the predict_proba method was returning int32 array instead of float64 for the stratified strategy. #13266 by Christos Aridas.

• Fix Fixed a bug in dummy.DummyClassifier where it was throwing a dimension mismatch error in prediction time if a column vector y with shape=(n, 1) was given at fit time. #13545 by Nick Sorros and Adrin Jalali.

sklearn.ensemble¶

• Major Feature Add two new implementations of gradient boosting trees: ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor. The implementation of these estimators is inspired by LightGBM and can be orders of magnitude faster than ensemble.GradientBoostingRegressor and ensemble.GradientBoostingClassifier when the number of samples is larger than tens of thousands of samples. The API of these new estimators is slightly different, and some of the features from ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor are not yet supported.

  These new estimators are experimental, which means that their results or their API might change without any deprecation cycle. To use them, you need to explicitly import enable_hist_gradient_boosting:

  >>> # explicitly require this experimental feature
  >>> from sklearn.experimental import enable_hist_gradient_boosting  # noqa
  >>> # now you can import normally from sklearn.ensemble
  >>> from sklearn.ensemble import HistGradientBoostingClassifier
  

  #12807 by Nicolas Hug.

• Feature Add ensemble.VotingRegressor which provides an equivalent of ensemble.VotingClassifier for regression problems. #12513 by Ramil Nugmanov and Mohamed Ali Jamaoui.

• Efficiency Make ensemble.IsolationForest prefer threads over processes when running with n_jobs > 1 as the underlying decision tree fit calls do release the GIL. This changes reduces memory usage and communication overhead. #12543 by Isaac Storch and Olivier Grisel.

• Efficiency Make ensemble.IsolationForest more memory efficient by avoiding keeping in memory each tree prediction. #13260 by Nicolas Goix.

• Efficiency ensemble.IsolationForest now uses chunks of data at prediction step, thus capping the memory usage. #13283 by Nicolas Goix.

• Efficiency sklearn.ensemble.GradientBoostingClassifier and sklearn.ensemble.GradientBoostingRegressor now keep the input y as float64 to avoid it being copied internally by trees. #13524 by Adrin Jalali.

• Enhancement Minimized the validation of X in ensemble.AdaBoostClassifier and ensemble.AdaBoostRegressor #13174 by Christos Aridas.

• Enhancement ensemble.IsolationForest now exposes warm_start parameter, allowing iterative addition of trees to an isolation forest. #13496 by Peter Marko.

• Fix The values of feature_importances_ in all random forest based models (i.e. ensemble.RandomForestClassifier, ensemble.RandomForestRegressor, ensemble.ExtraTreesClassifier, ensemble.ExtraTreesRegressor, ensemble.RandomTreesEmbedding, ensemble.GradientBoostingClassifier, and ensemble.GradientBoostingRegressor) now:

  • sum up to 1

  • all the single node trees in feature importance calculation are ignored

  • in case all trees have only one single node (i.e. a root node), feature importances will be an array of all zeros.

  #13636 and #13620 by Adrin Jalali.

• Fix Fixed a bug in ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor, which didn’t support scikit-learn estimators as the initial estimator. Also added support of initial estimator which does not support sample weights. #12436 by Jérémie du Boisberranger and #12983 by Nicolas Hug.

• Fix Fixed the output of the average path length computed in ensemble.IsolationForest when the input is either 0, 1 or 2. #13251 by Albert Thomas and joshuakennethjones.

• Fix Fixed a bug in ensemble.GradientBoostingClassifier where the gradients would be incorrectly computed in multiclass classification problems. #12715 by Nicolas Hug.

• Fix Fixed a bug in ensemble.GradientBoostingClassifier where validation sets for early stopping were not sampled with stratification. #13164 by Nicolas Hug.

• Fix Fixed a bug in ensemble.GradientBoostingClassifier where the default initial prediction of a multiclass classifier would predict the classes priors instead of the log of the priors. #12983 by Nicolas Hug.

• Fix Fixed a bug in ensemble.RandomForestClassifier where the predict method would error for multiclass multioutput forests models if any targets were strings. #12834 by Elizabeth Sander.

• Fix Fixed a bug in ensemble.gradient_boosting.LossFunction and ensemble.gradient_boosting.LeastSquaresError where the default value of learning_rate in update_terminal_regions is not consistent with the document and the caller functions. Note however that directly using these loss functions is deprecated. #6463 by movelikeriver.

• Fix ensemble.partial_dependence (and consequently the new version sklearn.inspection.partial_dependence) now takes sample weights into account for the partial dependence computation when the gradient boosting model has been trained with sample weights. #13193 by Samuel O. Ronsin.

• API Change ensemble.partial_dependence and ensemble.plot_partial_dependence are now deprecated in favor of inspection.partial_dependence and inspection.plot_partial_dependence. #12599 by Trevor Stephens and Nicolas Hug.

• Fix ensemble.VotingClassifier and ensemble.VotingRegressor were failing during fit in one of the estimators was set to None and sample_weight was not None. #13779 by Guillaume Lemaitre.

• API Change ensemble.VotingClassifier and ensemble.VotingRegressor accept 'drop' to disable an estimator in addition to None to be consistent with other estimators (i.e., pipeline.FeatureUnion and compose.ColumnTransformer). #13780 by Guillaume Lemaitre.

sklearn.externals¶

• API Change Deprecated externals.six since we have dropped support for Python 2.7. #12916 by Hanmin Qin.

sklearn.feature_extraction¶

• Fix If input='file' or input='filename', and a callable is given as the analyzer, sklearn.feature_extraction.text.HashingVectorizer, sklearn.feature_extraction.text.TfidfVectorizer, and sklearn.feature_extraction.text.CountVectorizer now read the data from the file(s) and then pass it to the given analyzer, instead of passing the file name(s) or the file object(s) to the analyzer. #13641 by Adrin Jalali.

sklearn.impute¶

• Major Feature Added impute.IterativeImputer, which is a strategy for imputing missing values by modeling each feature with missing values as a function of other features in a round-robin fashion. #8478 and #12177 by Sergey Feldman and Ben Lawson.

  The API of IterativeImputer is experimental and subject to change without any deprecation cycle. To use them, you need to explicitly import enable_iterative_imputer:

  >>> from sklearn.experimental import enable_iterative_imputer  # noqa
  >>> # now you can import normally from sklearn.impute
  >>> from sklearn.impute import IterativeImputer
  

• Feature The impute.SimpleImputer and impute.IterativeImputer have a new parameter 'add_indicator', which simply stacks a impute.MissingIndicator transform into the output of the imputer’s transform. That allows a predictive estimator to account for missingness. #12583, #13601 by Danylo Baibak.

• Fix In impute.MissingIndicator avoid implicit densification by raising an exception if input is sparse add missing_values property is set to 0. #13240 by Bartosz Telenczuk.

• Fix Fixed two bugs in impute.MissingIndicator. First, when X is sparse, all the non-zero non missing values used to become explicit False in the transformed data. Then, when features='missing-only', all features used to be kept if there were no missing values at all. #13562 by Jérémie du Boisberranger.

sklearn.inspection¶

(new subpackage)

• Feature Partial dependence plots (inspection.plot_partial_dependence) are now supported for any regressor or classifier (provided that they have a predict_proba method). #12599 by Trevor Stephens and Nicolas Hug.

sklearn.isotonic¶

• Feature Allow different dtypes (such as float32) in isotonic.IsotonicRegression. #8769 by Vlad Niculae

sklearn.linear_model¶

• Enhancement linear_model.Ridge now preserves float32 and float64 dtypes. #8769 and #11000 by Guillaume Lemaitre, and Joan Massich

• Feature linear_model.LogisticRegression and linear_model.LogisticRegressionCV now support Elastic-Net penalty, with the ‘saga’ solver. #11646 by Nicolas Hug.

• Feature Added linear_model.lars_path_gram, which is linear_model.lars_path in the sufficient stats mode, allowing users to compute linear_model.lars_path without providing X and y. #11699 by Kuai Yu.

• Efficiency linear_model.make_dataset now preserves float32 and float64 dtypes, reducing memory consumption in stochastic gradient, SAG and SAGA solvers. #8769 and #11000 by Nelle Varoquaux, Arthur Imbert, Guillaume Lemaitre, and Joan Massich

• Enhancement linear_model.LogisticRegression now supports an unregularized objective when penalty='none' is passed. This is equivalent to setting C=np.inf with l2 regularization. Not supported by the liblinear solver. #12860 by Nicolas Hug.

• Enhancement sparse_cg solver in linear_model.Ridge now supports fitting the intercept (i.e. fit_intercept=True) when inputs are sparse. #13336 by Bartosz Telenczuk.

• Enhancement The coordinate descent solver used in Lasso, ElasticNet, etc. now issues a ConvergenceWarning when it completes without meeting the desired toleranbce. #11754 and #13397 by Brent Fagan and Adrin Jalali.

• Fix Fixed a bug in linear_model.LogisticRegression and linear_model.LogisticRegressionCV with ‘saga’ solver, where the weights would not be correctly updated in some cases. #11646 by Tom Dupre la Tour.

• Fix Fixed the posterior mean, posterior covariance and returned regularization parameters in linear_model.BayesianRidge. The posterior mean and the posterior covariance were not the ones computed with the last update of the regularization parameters and the returned regularization parameters were not the final ones. Also fixed the formula of the log marginal likelihood used to compute the score when compute_score=True. #12174 by Albert Thomas.

• Fix Fixed a bug in linear_model.LassoLarsIC, where user input copy_X=False at instance creation would be overridden by default parameter value copy_X=True in fit. #12972 by Lucio Fernandez-Arjona

• Fix Fixed a bug in linear_model.LinearRegression that was not returning the same coeffecients and intercepts with fit_intercept=True in sparse and dense case. #13279 by Alexandre Gramfort

• Fix Fixed a bug in linear_model.HuberRegressor that was broken when X was of dtype bool. #13328 by Alexandre Gramfort.

• Fix Fixed a performance issue of saga and sag solvers when called in a joblib.Parallel setting with n_jobs > 1 and backend="threading", causing them to perform worse than in the sequential case. #13389 by Pierre Glaser.

• Fix Fixed a bug in linear_model.stochastic_gradient.BaseSGDClassifier that was not deterministic when trained in a multi-class setting on several threads. #13422 by Clément Doumouro.

• Fix Fixed bug in linear_model.ridge_regression, linear_model.Ridge and linear_model.RidgeClassifier that caused unhandled exception for arguments return_intercept=True and solver=auto (default) or any other solver different from sag. #13363 by Bartosz Telenczuk

• Fix linear_model.ridge_regression will now raise an exception if return_intercept=True and solver is different from sag. Previously, only warning was issued. #13363 by Bartosz Telenczuk

• Fix linear_model.ridge_regression will choose sparse_cg solver for sparse inputs when solver=auto and sample_weight is provided (previously cholesky solver was selected). #13363 by Bartosz Telenczuk

• API Change The use of linear_model.lars_path with X=None while passing Gram is deprecated in version 0.21 and will be removed in version 0.23. Use linear_model.lars_path_gram instead. #11699 by Kuai Yu.

• API Change linear_model.logistic_regression_path is deprecated in version 0.21 and will be removed in version 0.23. #12821 by Nicolas Hug.

• Fix linear_model.RidgeCV with generalized cross-validation now correctly fits an intercept when fit_intercept=True and the design matrix is sparse. #13350 by Jérôme Dockès

sklearn.manifold¶

• Efficiency Make manifold.tsne.trustworthiness use an inverted index instead of an np.where lookup to find the rank of neighbors in the input space. This improves efficiency in particular when computed with lots of neighbors and/or small datasets. #9907 by William de Vazelhes.

sklearn.metrics¶

• Feature Added the metrics.max_error metric and a corresponding 'max_error' scorer for single output regression. #12232 by Krishna Sangeeth.

• Feature Add metrics.multilabel_confusion_matrix, which calculates a confusion matrix with true positive, false positive, false negative and true negative counts for each class. This facilitates the calculation of set-wise metrics such as recall, specificity, fall out and miss rate. #11179 by Shangwu Yao and Joel Nothman.

• Feature metrics.jaccard_score has been added to calculate the Jaccard coefficient as an evaluation metric for binary, multilabel and multiclass tasks, with an interface analogous to metrics.f1_score. #13151 by Gaurav Dhingra and Joel Nothman.

• Feature Added metrics.pairwise.haversine_distances which can be accessed with metric='pairwise' through metrics.pairwise_distances and estimators. (Haversine distance was previously available for nearest neighbors calculation.) #12568 by Wei Xue, Emmanuel Arias and Joel Nothman.

• Efficiency Faster metrics.pairwise_distances with n_jobs > 1 by using a thread-based backend, instead of process-based backends. #8216 by Pierre Glaser and Romuald Menuet

• Efficiency The pairwise manhattan distances with sparse input now uses the BLAS shipped with scipy instead of the bundled BLAS. #12732 by Jérémie du Boisberranger

• Enhancement Use label accuracy instead of micro-average on metrics.classification_report to avoid confusion. micro-average is only shown for multi-label or multi-class with a subset of classes because it is otherwise identical to accuracy. #12334 by Emmanuel Arias, Joel Nothman and Andreas Müller

• Enhancement Added beta parameter to metrics.homogeneity_completeness_v_measure and metrics.v_measure_score to configure the tradeoff between homogeneity and completeness. #13607 by Stephane Couvreur and and Ivan Sanchez.

• Fix The metric metrics.r2_score is degenerate with a single sample and now it returns NaN and raises exceptions.UndefinedMetricWarning. #12855 by Pawel Sendyk.

• Fix Fixed a bug where metrics.brier_score_loss will sometimes return incorrect result when there’s only one class in y_true. #13628 by Hanmin Qin.

• Fix Fixed a bug in metrics.label_ranking_average_precision_score where sample_weight wasn’t taken into account for samples with degenerate labels. #13447 by Dan Ellis.

• API Change The parameter labels in metrics.hamming_loss is deprecated in version 0.21 and will be removed in version 0.23. #10580 by Reshama Shaikh and Sandra Mitrovic.

• Fix The function metrics.pairwise.euclidean_distances, and therefore several estimators with metric='euclidean', suffered from numerical precision issues with float32 features. Precision has been increased at the cost of a small drop of performance. #13554 by @Celelibi and Jérémie du Boisberranger.

• API Change metrics.jaccard_similarity_score is deprecated in favour of the more consistent metrics.jaccard_score. The former behavior for binary and multiclass targets is broken. #13151 by Joel Nothman.

sklearn.mixture¶

• Fix Fixed a bug in mixture.BaseMixture and therefore on estimators based on it, i.e. mixture.GaussianMixture and mixture.BayesianGaussianMixture, where fit_predict and fit.predict were not equivalent. #13142 by Jérémie du Boisberranger.

sklearn.model_selection¶

• Feature Classes GridSearchCV and RandomizedSearchCV now allow for refit=callable to add flexibility in identifying the best estimator. See Balance model complexity and cross-validated score. #11354 by Wenhao Zhang, Joel Nothman and Adrin Jalali.

• Enhancement Classes GridSearchCV, RandomizedSearchCV, and methods cross_val_score, cross_val_predict, cross_validate, now print train scores when return_train_scores is True and verbose > 2. For learning_curve, and validation_curve only the latter is required. #12613 and #12669 by Marc Torrellas.

• Enhancement Some CV splitter classes and model_selection.train_test_split now raise ValueError when the resulting training set is empty. #12861 by Nicolas Hug.

• Fix Fixed a bug where model_selection.StratifiedKFold shuffles each class’s samples with the same random_state, making shuffle=True ineffective. #13124 by Hanmin Qin.

• Fix Added ability for model_selection.cross_val_predict to handle multi-label (and multioutput-multiclass) targets with predict_proba-type methods. #8773 by Stephen Hoover.

• Fix Fixed an issue in cross_val_predict where method="predict_proba" returned always 0.0 when one of the classes was excluded in a cross-validation fold. #13366 by Guillaume Fournier

sklearn.multiclass¶

• Fix Fixed an issue in multiclass.OneVsOneClassifier.decision_function where the decision_function value of a given sample was different depending on whether the decision_function was evaluated on the sample alone or on a batch containing this same sample due to the scaling used in decision_function. #10440 by Jonathan Ohayon.

sklearn.multioutput¶

• Fix Fixed a bug in multioutput.MultiOutputClassifier where the predict_proba method incorrectly checked for predict_proba attribute in the estimator object. #12222 by Rebekah Kim

sklearn.neighbors¶

• Major Feature Added neighbors.NeighborhoodComponentsAnalysis for metric learning, which implements the Neighborhood Components Analysis algorithm. #10058 by William de Vazelhes and John Chiotellis.

• API Change Methods in neighbors.NearestNeighbors : kneighbors, radius_neighbors, kneighbors_graph, radius_neighbors_graph now raise NotFittedError, rather than AttributeError, when called before fit #12279 by Krishna Sangeeth.

sklearn.neural_network¶

• Fix Fixed a bug in neural_network.MLPClassifier and neural_network.MLPRegressor where the option shuffle=False was being ignored. #12582 by Sam Waterbury.

• Fix Fixed a bug in neural_network.MLPClassifier where validation sets for early stopping were not sampled with stratification. In the multilabel case however, splits are still not stratified. #13164 by Nicolas Hug.

sklearn.pipeline¶

• Feature pipeline.Pipeline can now use indexing notation (e.g. my_pipeline[0:-1]) to extract a subsequence of steps as another Pipeline instance. A Pipeline can also be indexed directly to extract a particular step (e.g. my_pipeline['svc']), rather than accessing named_steps. #2568 by Joel Nothman.

• Feature Added optional parameter verbose in pipeline.Pipeline, compose.ColumnTransformer and pipeline.FeatureUnion and corresponding make_ helpers for showing progress and timing of each step. #11364 by Baze Petrushev, Karan Desai, Joel Nothman, and Thomas Fan.

• Enhancement pipeline.Pipeline now supports using 'passthrough' as a transformer, with the same effect as None. #11144 by Thomas Fan.

• Enhancement pipeline.Pipeline implements __len__ and therefore len(pipeline) returns the number of steps in the pipeline. #13439 by Lakshya KD.

sklearn.preprocessing¶

• Feature preprocessing.OneHotEncoder now supports dropping one feature per category with a new drop parameter. #12908 by Drew Johnston.

• Efficiency preprocessing.OneHotEncoder and preprocessing.OrdinalEncoder now handle pandas DataFrames more efficiently. #13253 by @maikia.

• Efficiency Make preprocessing.MultiLabelBinarizer cache class mappings instead of calculating it every time on the fly. #12116 by Ekaterina Krivich and Joel Nothman.

• Efficiency preprocessing.PolynomialFeatures now supports compressed sparse row (CSR) matrices as input for degrees 2 and 3. This is typically much faster than the dense case as it scales with matrix density and expansion degree (on the order of density^degree), and is much, much faster than the compressed sparse column (CSC) case. #12197 by Andrew Nystrom.

• Efficiency Speed improvement in preprocessing.PolynomialFeatures, in the dense case. Also added a new parameter order which controls output order for further speed performances. #12251 by Tom Dupre la Tour.

• Fix Fixed the calculation overflow when using a float16 dtype with preprocessing.StandardScaler. #13007 by Raffaello Baluyot

• Fix Fixed a bug in preprocessing.QuantileTransformer and preprocessing.quantile_transform to force n_quantiles to be at most equal to n_samples. Values of n_quantiles larger than n_samples were either useless or resulting in a wrong approximation of the cumulative distribution function estimator. #13333 by Albert Thomas.

• API Change The default value of copy in preprocessing.quantile_transform will change from False to True in 0.23 in order to make it more consistent with the default copy values of other functions in preprocessing and prevent unexpected side effects by modifying the value of X inplace. #13459 by Hunter McGushion.

sklearn.svm¶

• Fix Fixed an issue in svm.SVC.decision_function when decision_function_shape='ovr'. The decision_function value of a given sample was different depending on whether the decision_function was evaluated on the sample alone or on a batch containing this same sample due to the scaling used in decision_function. #10440 by Jonathan Ohayon.

sklearn.tree¶

• Feature Decision Trees can now be plotted with matplotlib using tree.plot_tree without relying on the dot library, removing a hard-to-install dependency. #8508 by Andreas Müller.

• Feature Decision Trees can now be exported in a human readable textual format using tree.export_text. #6261 by Giuseppe Vettigli <JustGlowing>.

• Feature get_n_leaves() and get_depth() have been added to tree.BaseDecisionTree and consequently all estimators based on it, including tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier, and tree.ExtraTreeRegressor. #12300 by Adrin Jalali.

• Fix Trees and forests did not previously predict multi-output classification targets with string labels, despite accepting them in fit. #11458 by Mitar Milutinovic.

• Fix Fixed an issue with tree.BaseDecisionTree and consequently all estimators based on it, including tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier, and tree.ExtraTreeRegressor, where they used to exceed the given max_depth by 1 while expanding the tree if max_leaf_nodes and max_depth were both specified by the user. Please note that this also affects all ensemble methods using decision trees. #12344 by Adrin Jalali.

sklearn.utils¶

• Feature utils.resample now accepts a stratify parameter for sampling according to class distributions. #13549 by Nicolas Hug.

• API Change Deprecated warn_on_dtype parameter from utils.check_array and utils.check_X_y. Added explicit warning for dtype conversion in check_pairwise_arrays if the metric being passed is a pairwise boolean metric. #13382 by Prathmesh Savale.

Multiple modules¶

• Major Feature The __repr__() method of all estimators (used when calling print(estimator)) has been entirely re-written, building on Python’s pretty printing standard library. All parameters are printed by default, but this can be altered with the print_changed_only option in sklearn.set_config. #11705 by Nicolas Hug.

• Major Feature Add estimators tags: these are annotations of estimators that allow programmatic inspection of their capabilities, such as sparse matrix support, supported output types and supported methods. Estimator tags also determine the tests that are run on an estimator when check_estimator is called. Read more in the User Guide. #8022 by Andreas Müller.

• Efficiency Memory copies are avoided when casting arrays to a different dtype in multiple estimators. #11973 by Roman Yurchak.

• Fix Fixed a bug in the implementation of the our_rand_r helper function that was not behaving consistently across platforms. #13422 by Madhura Parikh and Clément Doumouro.

Miscellaneous¶

• Enhancement Joblib is no longer vendored in scikit-learn, and becomes a dependency. Minimal supported version is joblib 0.11, however using version >= 0.13 is strongly recommended. #13531 by Roman Yurchak.