Version 1.8

Legend for changelogs

• Major Feature something big that you couldn’t do before.

• Feature something that you couldn’t do before.

• Efficiency an existing feature now may not require as much computation or memory.

• Enhancement a miscellaneous minor improvement.

• Fix something that previously didn’t work as documented – or according to reasonable expectations – should now work.

• API Change you will need to change your code to have the same effect in the future; or a feature will be removed in the future.

Version 1.8.0

December 2025

Changes impacting many modules

• Efficiency Improved CPU and memory usage in estimators and metric functions that rely on weighted percentiles and better match NumPy and Scipy (un-weighted) implementations of percentiles. By Lucy Liu #31775

Support for Array API

Additional estimators and functions have been updated to include support for all Array API compliant inputs.

See Array API support (experimental) for more details.

• Feature sklearn.preprocessing.StandardScaler now supports Array API compliant inputs. By Alexander Fabisch, Edoardo Abati, Olivier Grisel and Charles Hill. #27113

• Feature linear_model.RidgeCV, linear_model.RidgeClassifier and linear_model.RidgeClassifierCV now support array API compatible inputs with solver="svd". By Jérôme Dockès. #27961

• Feature metrics.pairwise.pairwise_kernels for any kernel except “laplacian” and metrics.pairwise_distances for metrics “cosine”, “euclidean” and “l2” now support array API inputs. By Emily Chen and Lucy Liu #29822

• Feature sklearn.metrics.confusion_matrix now supports Array API compatible inputs. By Stefanie Senger #30562

• Feature sklearn.mixture.GaussianMixture with init_params="random" or init_params="random_from_data" and warm_start=False now supports Array API compatible inputs. By Stefanie Senger and Loïc Estève #30777

• Feature sklearn.metrics.roc_curve now supports Array API compatible inputs. By Thomas Li #30878

• Feature preprocessing.PolynomialFeatures now supports array API compatible inputs. By Omar Salman #31580

• Feature calibration.CalibratedClassifierCV now supports array API compatible inputs with method="temperature" and when the underlying estimator also supports the array API. By Omar Salman #32246

• Feature sklearn.metrics.precision_recall_curve now supports array API compatible inputs. By Lucy Liu #32249

• Feature sklearn.model_selection.cross_val_predict now supports array API compatible inputs. By Omar Salman #32270

• Feature sklearn.metrics.brier_score_loss, sklearn.metrics.log_loss, sklearn.metrics.d2_brier_score and sklearn.metrics.d2_log_loss_score now support array API compatible inputs. By Omar Salman #32422

• Feature naive_bayes.GaussianNB now supports array API compatible inputs. By Omar Salman #32497

• Feature preprocessing.LabelBinarizer and preprocessing.label_binarize now support numeric array API compatible inputs with sparse_output=False. By Virgil Chan. #32582

• Feature sklearn.metrics.det_curve now supports Array API compliant inputs. By Josef Affourtit. #32586

• Feature sklearn.metrics.pairwise.manhattan_distances now supports array API compatible inputs. By Omar Salman. #32597

• Feature sklearn.metrics.calinski_harabasz_score now supports Array API compliant inputs. By Josef Affourtit. #32600

• Feature sklearn.metrics.balanced_accuracy_score now supports array API compatible inputs. By Omar Salman. #32604

• Feature sklearn.metrics.pairwise.laplacian_kernel now supports array API compatible inputs. By Zubair Shakoor. #32613

• Feature sklearn.metrics.cohen_kappa_score now supports array API compatible inputs. By Omar Salman. #32619

• Feature sklearn.metrics.cluster.davies_bouldin_score now supports Array API compliant inputs. By Josef Affourtit. #32693

• Fix Estimators with array API support no longer reject dataframe inputs when array API support is enabled. By Tim Head #32838

Metadata routing

Refer to the Metadata Routing User Guide for more details.

• Fix Fixed an issue where passing sample_weight to a Pipeline inside a GridSearchCV would raise an error with metadata routing enabled. By Adrin Jalali. #31898

Free-threaded CPython 3.14 support

scikit-learn has support for free-threaded CPython, in particular free-threaded wheels are available for all of our supported platforms on Python 3.14.

Free-threaded (also known as nogil) CPython is a version of CPython that aims at enabling efficient multi-threaded use cases by removing the Global Interpreter Lock (GIL).

If you want to try out free-threaded Python, the recommendation is to use Python 3.14, that has fixed a number of issues compared to Python 3.13. Feel free to try free-threaded on your use case and report any issues!

For more details about free-threaded CPython see py-free-threading doc, in particular how to install a free-threaded CPython and Ecosystem compatibility tracking.

By Loïc Estève and Olivier Grisel and many other people in the wider Scientific Python and CPython ecosystem, for example Nathan Goldbaum, Ralf Gommers, Edgar Andrés Margffoy Tuay. #32079

sklearn.base

• Feature Refactored dir in BaseEstimator to recognize condition check in available_if. By John Hendricks and Miguel Parece. #31928

• Fix Fixed the handling of pandas missing values in HTML display of all estimators. By Dea María Léon. #32341

sklearn.calibration

• Feature Added temperature scaling method in calibration.CalibratedClassifierCV. By Virgil Chan and Christian Lorentzen. #31068

sklearn.cluster

• Efficiency cluster.kmeans_plusplus now uses np.cumsum directly without extra numerical stability checks and without casting to np.float64. By Tiziano Zito #31991

• Fix The default value of the copy parameter in cluster.HDBSCAN will change from False to True in 1.10 to avoid data modification and maintain consistency with other estimators. By Sarthak Puri. #31973

sklearn.compose

• Fix The compose.ColumnTransformer now correctly fits on data provided as a polars.DataFrame when any transformer has a sparse output. By Phillipp Gnan. #32188

sklearn.covariance

• Efficiency sklearn.covariance.GraphicalLasso, sklearn.covariance.GraphicalLassoCV and sklearn.covariance.graphical_lasso with mode="cd" profit from the fit time performance improvement of sklearn.linear_model.Lasso by means of gap safe screening rules. By Christian Lorentzen. #31987

• Fix Fixed uncontrollable randomness in sklearn.covariance.GraphicalLasso, sklearn.covariance.GraphicalLassoCV and sklearn.covariance.graphical_lasso. For mode="cd", they now use cyclic coordinate descent. Before, it was random coordinate descent with uncontrollable random number seeding. By Christian Lorentzen. #31987

• Fix Added correction to covariance.MinCovDet to adjust for consistency at the normal distribution. This reduces the bias present when applying this method to data that is normally distributed. By Daniel Herrera-Esposito #32117

sklearn.decomposition

• Efficiency sklearn.decomposition.DictionaryLearning and sklearn.decomposition.MiniBatchDictionaryLearning with fit_algorithm="cd", sklearn.decomposition.SparseCoder with transform_algorithm="lasso_cd", sklearn.decomposition.MiniBatchSparsePCA, sklearn.decomposition.SparsePCA, sklearn.decomposition.dict_learning and sklearn.decomposition.dict_learning_online with method="cd", sklearn.decomposition.sparse_encode with algorithm="lasso_cd" all profit from the fit time performance improvement of sklearn.linear_model.Lasso by means of gap safe screening rules. By Christian Lorentzen. #31987

• Enhancement decomposition.SparseCoder now follows the transformer API of scikit-learn. In addition, the fit method now validates the input and parameters. By François Paugam. #32077

• Fix Add input checks to the inverse_transform method of decomposition.PCA and decomposition.IncrementalPCA. #29310 by Ian Faust. #29310

sklearn.discriminant_analysis

• Feature Added solver, covariance_estimator and shrinkage in discriminant_analysis.QuadraticDiscriminantAnalysis. The resulting class is more similar to discriminant_analysis.LinearDiscriminantAnalysis and allows for more flexibility in the estimation of the covariance matrices. By Daniel Herrera-Esposito. #32108

sklearn.ensemble

• Fix ensemble.BaggingClassifier, ensemble.BaggingRegressor and ensemble.IsolationForest now use sample_weight to draw the samples instead of forwarding them multiplied by a uniformly sampled mask to the underlying estimators. Furthermore, when max_samples is a float, it is now interpreted as a fraction of sample_weight.sum() instead of X.shape[0]. The new default max_samples=None draws X.shape[0] samples, irrespective of sample_weight. By Antoine Baker. #31414 and #32825

sklearn.feature_selection

• Enhancement feature_selection.SelectFromModel now does not force max_features to be less than or equal to the number of input features. By Thibault #31939

sklearn.gaussian_process

• Efficiency make GaussianProcessRegressor.predict faster when return_cov and return_std are both False. By Rafael Ayllón Gavilán. #31431

sklearn.linear_model

• Efficiency linear_model.ElasticNet and linear_model.Lasso with precompute=False use less memory for dense X and are a bit faster. Previously, they used twice the memory of X even for Fortran-contiguous X. By Christian Lorentzen #31665

• Efficiency linear_model.ElasticNet and linear_model.Lasso avoid double input checking and are therefore a bit faster. By Christian Lorentzen. #31848

• Efficiency linear_model.ElasticNet, linear_model.ElasticNetCV, linear_model.Lasso, linear_model.LassoCV, linear_model.MultiTaskElasticNet, linear_model.MultiTaskElasticNetCV, linear_model.MultiTaskLasso and linear_model.MultiTaskLassoCV are faster to fit by avoiding a BLAS level 1 (axpy) call in the innermost loop. Same for functions linear_model.enet_path and linear_model.lasso_path. By Christian Lorentzen #31956 and #31880

• Efficiency linear_model.ElasticNetCV, linear_model.LassoCV, linear_model.MultiTaskElasticNetCV and linear_model.MultiTaskLassoCV avoid an additional copy of X with default copy_X=True. By Christian Lorentzen. #31946

• Efficiency linear_model.ElasticNet, linear_model.ElasticNetCV, linear_model.Lasso, linear_model.LassoCV, linear_model.MultiTaskElasticNet, linear_model.MultiTaskElasticNetCV linear_model.MultiTaskLasso, linear_model.MultiTaskLassoCV as well as linear_model.lasso_path and linear_model.enet_path now implement gap safe screening rules in the coordinate descent solver for dense and sparse X. The speedup of fitting time is particularly pronounced (10-times is possible) when computing regularization paths like the *CV-variants of the above estimators do. There is now an additional check of the stopping criterion before entering the main loop of descent steps. As the stopping criterion requires the computation of the dual gap, the screening happens whenever the dual gap is computed. By Christian Lorentzen #31882, #31986, #31987 and #32014

• Enhancement linear_model.ElasticNet, linear_model.ElasticNetCV, linear_model.Lasso, linear_model.LassoCV, MultiTaskElasticNet, MultiTaskElasticNetCV, MultiTaskLasso, MultiTaskLassoCV, as well as linear_model.enet_path and linear_model.lasso_path now use dual gap <= tol instead of dual gap < tol as stopping criterion. The resulting coefficients might differ to previous versions of scikit-learn in rare cases. By Christian Lorentzen. #31906

• Fix Fix the convergence criteria for SGD models, to avoid premature convergence when tol != None. This primarily impacts SGDOneClassSVM but also affects SGDClassifier and SGDRegressor. Before this fix, only the loss function without penalty was used as the convergence check, whereas now, the full objective with regularization is used. By Guillaume Lemaitre and kostayScr #31856

• Fix The allowed parameter range for the initial learning rate eta0 in linear_model.SGDClassifier, linear_model.SGDOneClassSVM, linear_model.SGDRegressor and linear_model.Perceptron changed from non-negative numbers to strictly positive numbers. As a consequence, the default eta0 of linear_model.SGDClassifier and linear_model.SGDOneClassSVM changed from 0 to 0.01. But note that eta0 is not used by the default learning rate “optimal” of those two estimators. By Christian Lorentzen. #31933

• Fix linear_model.LogisticRegressionCV is able to handle CV splits where some class labels are missing in some folds. Before, it raised an error whenever a class label were missing in a fold. By Christian Lorentzen. #32747

• API Change linear_model.PassiveAggressiveClassifier and linear_model.PassiveAggressiveRegressor are deprecated and will be removed in 1.10. Equivalent estimators are available with linear_model.SGDClassifier and SGDRegressor, both of which expose the options learning_rate="pa1" and "pa2". The parameter eta0 can be used to specify the aggressiveness parameter of the Passive-Aggressive-Algorithms, called C in the reference paper. By Christian Lorentzen #31932 and #29097

• API Change linear_model.SGDClassifier, linear_model.SGDRegressor, and linear_model.SGDOneClassSVM now deprecate negative values for the power_t parameter. Using a negative value will raise a warning in version 1.8 and will raise an error in version 1.10. A value in the range [0.0, inf) must be used instead. By Ritvi Alagusankar #31474

• API Change Raising error in sklearn.linear_model.LogisticRegression when liblinear solver is used and input X values are larger than 1e30, the liblinear solver freezes otherwise. By Shruti Nath. #31888

• API Change linear_model.LogisticRegressionCV got a new parameter use_legacy_attributes to control the types and shapes of the fitted attributes C_, l1_ratio_, coefs_paths_, scores_ and n_iter_. The current default value True keeps the legacy behaviour. If False then:

  • C_ is a float.

  • l1_ratio_ is a float.

  • coefs_paths_ is an ndarray of shape (n_folds, n_l1_ratios, n_cs, n_classes, n_features). For binary problems (n_classes=2), the 2nd last dimension is 1.

  • scores_ is an ndarray of shape (n_folds, n_l1_ratios, n_cs).

  • n_iter_ is an ndarray of shape (n_folds, n_l1_ratios, n_cs).

  In version 1.10, the default will change to False and use_legacy_attributes will be deprecated. In 1.12 use_legacy_attributes will be removed. By Christian Lorentzen. #32114

• API Change Parameter penalty of linear_model.LogisticRegression and linear_model.LogisticRegressionCV is deprecated and will be removed in version 1.10. The equivalent behaviour can be obtained as follows:

  • for linear_model.LogisticRegression

    • use l1_ratio=0 instead of penalty="l2"

    • use l1_ratio=1 instead of penalty="l1"

    • use 0<l1_ratio<1 instead of penalty="elasticnet"

    • use C=np.inf instead of penalty=None

  • for linear_model.LogisticRegressionCV

    • use l1_ratios=(0,) instead of penalty="l2"

    • use l1_ratios=(1,) instead of penalty="l1"

    • the equivalent of penalty=None is to have np.inf as an element of the Cs parameter

  For linear_model.LogisticRegression, the default value of l1_ratio has changed from None to 0.0. Setting l1_ratio=None is deprecated and will raise an error in version 1.10

  For linear_model.LogisticRegressionCV, the default value of l1_ratios has changed from None to "warn". It will be changed to (0,) in version 1.10. Setting l1_ratios=None is deprecated and will raise an error in version 1.10.

  By Christian Lorentzen. #32659

• API Change The n_jobs parameter of linear_model.LogisticRegression is deprecated and will be removed in 1.10. It has no effect since 1.8. By Loïc Estève. #32742

sklearn.manifold

• Major Feature manifold.ClassicalMDS was implemented to perform classical MDS (eigendecomposition of the double-centered distance matrix). By Dmitry Kobak and Meekail Zain #31322

• Feature manifold.MDS now supports arbitrary distance metrics (via metric and metric_params parameters) and initialization via classical MDS (via init parameter). The dissimilarity parameter was deprecated. The old metric parameter was renamed into metric_mds. By Dmitry Kobak #32229

• Feature manifold.TSNE now supports PCA initialization with sparse input matrices. By Arturo Amor. #32433

sklearn.metrics

• Feature metrics.d2_brier_score has been added which calculates the D^2 for the Brier score. By Omar Salman. #28971

• Feature Add metrics.confusion_matrix_at_thresholds function that returns the number of true negatives, false positives, false negatives and true positives per threshold. By Success Moses. #30134

• Efficiency Avoid redundant input validation in metrics.d2_log_loss_score leading to a 1.2x speedup in large scale benchmarks. By Olivier Grisel and Omar Salman #32356

• Enhancement metrics.median_absolute_error now supports Array API compatible inputs. By Lucy Liu. #31406

• Enhancement Improved the error message for sparse inputs for the following metrics: metrics.accuracy_score, metrics.multilabel_confusion_matrix, metrics.jaccard_score, metrics.zero_one_loss, metrics.f1_score, metrics.fbeta_score, metrics.precision_recall_fscore_support, metrics.class_likelihood_ratios, metrics.precision_score, metrics.recall_score, metrics.classification_report, metrics.hamming_loss. By Lucy Liu. #32047

• Fix metrics.median_absolute_error now uses _averaged_weighted_percentile instead of _weighted_percentile to calculate median when sample_weight is not None. This is equivalent to using the “averaged_inverted_cdf” instead of the “inverted_cdf” quantile method, which gives results equivalent to numpy.median if equal weights used. By Lucy Liu #30787

• Fix Additional sample_weight checking has been added to metrics.accuracy_score, metrics.balanced_accuracy_score, metrics.brier_score_loss, metrics.class_likelihood_ratios, metrics.classification_report, metrics.cohen_kappa_score, metrics.confusion_matrix, metrics.f1_score, metrics.fbeta_score, metrics.hamming_loss, metrics.jaccard_score, metrics.matthews_corrcoef, metrics.multilabel_confusion_matrix, metrics.precision_recall_fscore_support, metrics.precision_score, metrics.recall_score and metrics.zero_one_loss. sample_weight can only be 1D, consistent to y_true and y_pred in length,and all values must be finite and not complex. By Lucy Liu. #31701

• Fix y_pred is deprecated in favour of y_score in metrics.DetCurveDisplay.from_predictions and metrics.PrecisionRecallDisplay.from_predictions. y_pred will be removed in v1.10. By Luis #31764

• Fix repr on a scorer which has been created with a partial score_func now correctly works and uses the repr of the given partial object. By Adrin Jalali. #31891

• Fix kwargs specified in the curve_kwargs parameter of metrics.RocCurveDisplay.from_cv_results now only overwrite their corresponding default value before being passed to Matplotlib’s plot. Previously, passing any curve_kwargs would overwrite all default kwargs. By Lucy Liu. #32313

• Fix Registered named scorer objects for metrics.d2_brier_score and metrics.d2_log_loss_score and updated their input validation to be consistent with related metric functions. By Olivier Grisel and Omar Salman #32356

• Fix metrics.RocCurveDisplay.from_cv_results will now infer pos_label as estimator.classes_[-1], using the estimator from cv_results, when pos_label=None. Previously, an error was raised when pos_label=None. By Lucy Liu. #32372

• Fix All classification metrics now raise a ValueError when required input arrays (y_pred, y_true, y1, y2, pred_decision, or y_proba) are empty. Previously, accuracy_score, class_likelihood_ratios, classification_report, confusion_matrix, hamming_loss, jaccard_score, matthews_corrcoef, multilabel_confusion_matrix, and precision_recall_fscore_support did not raise this error consistently. By Stefanie Senger. #32549

• API Change metrics.cluster.entropy is deprecated and will be removed in v1.10. By Lucy Liu #31294

• API Change The estimator_name parameter is deprecated in favour of name in metrics.PrecisionRecallDisplay and will be removed in 1.10. By Lucy Liu. #32310

sklearn.model_selection

• Enhancement model_selection.StratifiedShuffleSplit will now specify which classes

  have too few members when raising a ValueError if any class has less than 2 members. This is useful to identify which classes are causing the error. By Marc Bresson #32265

• Fix Fix shuffle behaviour in model_selection.StratifiedGroupKFold. Now stratification among folds is also preserved when shuffle=True. By Pau Folch. #32540

sklearn.multiclass

• Fix Fix tie-breaking behavior in multiclass.OneVsRestClassifier to match np.argmax tie-breaking behavior. By Lakshmi Krishnan. #15504

sklearn.naive_bayes

• Fix naive_bayes.GaussianNB preserves the dtype of the fitted attributes according to the dtype of X. By Omar Salman #32497

sklearn.preprocessing

• Enhancement preprocessing.SplineTransformer can now handle missing values with the parameter handle_missing. By Stefanie Senger. #28043

• Enhancement The preprocessing.PowerTransformer now returns a warning when NaN values are encountered in the inverse transform, inverse_transform, typically caused by extremely skewed data. By Roberto Mourao #29307

• Enhancement preprocessing.MaxAbsScaler can now clip out-of-range values in held-out data with the parameter clip. By Hleb Levitski. #31790

• Fix Fixed a bug in preprocessing.OneHotEncoder where handle_unknown='warn' incorrectly behaved like 'ignore' instead of 'infrequent_if_exist'. By Nithurshen #32592

sklearn.semi_supervised

• Fix User written kernel results are now normalized in semi_supervised.LabelPropagation so all row sums equal 1 even if kernel gives asymmetric or non-uniform row sums. By Dan Schult. #31924

sklearn.tree

• Efficiency tree.DecisionTreeRegressor with criterion="absolute_error" now runs much faster: O(n log n) complexity against previous O(n^2) allowing to scale to millions of data points, even hundred of millions. By Arthur Lacote #32100

• Fix Make tree.export_text thread-safe. By Olivier Grisel. #30041

• Fix export_graphviz now raises a ValueError if given feature names are not all strings. By Guilherme Peixoto #31036

• Fix tree.DecisionTreeRegressor with criterion="absolute_error" would sometimes make sub-optimal splits (i.e. splits that don’t minimize the absolute error). Now it’s fixed. Hence retraining trees might gives slightly different results. By Arthur Lacote #32100

• Fix Fixed a regression in decision trees where almost constant features were not handled properly. By Sercan Turkmen. #32259

• Fix Fixed splitting logic during training in tree.DecisionTree* (and consequently in ensemble.RandomForest*) for nodes containing near-constant feature values and missing values. Beforehand, trees were cut short if a constant feature was found, even if there was more splitting that could be done on the basis of missing values. By Arthur Lacote #32274

• Fix Fix handling of missing values in method decision_path of trees (tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier and tree.ExtraTreeRegressor) By Arthur Lacote. #32280

• Fix Fix decision tree splitting with missing values present in some features. In some cases the last non-missing sample would not be partitioned correctly. By Tim Head and Arthur Lacote. #32351

sklearn.utils

• Efficiency The function sklearn.utils.extmath.safe_sparse_dot was improved by a dedicated Cython routine for the case of a @ b with sparse 2-dimensional a and b and when a dense output is required, i.e., dense_output=True. This improves several algorithms in scikit-learn when dealing with sparse arrays (or matrices). By Christian Lorentzen. #31952

• Enhancement The parameter table in the HTML representation of all scikit-learn estimators and more generally of estimators inheriting from base.BaseEstimator now displays the parameter description as a tooltip and has a link to the online documentation for each parameter. By Dea María Léon. #31564

• Enhancement sklearn.utils._check_sample_weight now raises a clearer error message when the provided weights are neither a scalar nor a 1-D array-like of the same size as the input data. By Kapil Parekh. #31873

• Enhancement sklearn.utils.estimator_checks.parametrize_with_checks now lets you configure strict mode for xfailing checks. Tests that unexpectedly pass will lead to a test failure. The default behaviour is unchanged. By Tim Head. #31951

• Enhancement Fixed the alignment of the “?” and “i” symbols and improved the color style of the HTML representation of estimators. By Guillaume Lemaitre. #31969

• Fix Changes the way color are chosen when displaying an estimator as an HTML representation. Colors are not adapted anymore to the user’s theme, but chosen based on theme declared color scheme (light or dark) for VSCode and JupyterLab. If theme does not declare a color scheme, scheme is chosen according to default text color of the page, if it fails fallbacks to a media query. By Matt J.. #32330

• API Change utils.extmath.stable_cumsum is deprecated and will be removed in v1.10. Use np.cumulative_sum with the desired dtype directly instead. By Tiziano Zito. #32258

Code and documentation contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.7, including:

$id, 4hm3d, Acciaro Gennaro Daniele, achyuthan.s, Adam J. Stewart, Adriano Leão, Adrien Linares, Adrin Jalali, Aitsaid Azzedine Idir, Alexander Fabisch, Alexandre Abraham, Andrés H. Zapke, Anne Beyer, Anthony Gitter, AnthonyPrudent, antoinebaker, Arpan Mukherjee, Arthur, Arthur Lacote, Arturo Amor, ayoub.agouzoul, Ayrat, Ayush, Ayush Tanwar, Basile Jezequel, Bhavya Patwa, BRYANT MUSI BABILA, Casey Heath, Chems Ben, Christian Lorentzen, Christian Veenhuis, Christine P. Chai, cstec, C. Titus Brown, Daniel Herrera-Esposito, Dan Schult, dbXD320, Dea María Léon, Deepyaman Datta, dependabot[bot], Dhyey Findoriya, Dimitri Papadopoulos Orfanos, Dipak Dhangar, Dmitry Kobak, elenafillo, Elham Babaei, EmilyXinyi, Emily (Xinyi) Chen, Eugen-Bleck, Evgeni Burovski, fabarca, Fabrizio Damicelli, Faizan-Ul Huda, François Goupil, François Paugam, Gaetan, GaetandeCast, Gesa Loof, Gonçalo Guiomar, Gordon Grey, Gowtham Kumar K., Guilherme Peixoto, Guillaume Lemaitre, hakan çanakçı, Harshil Sanghvi, Henri Bonamy, Hleb Levitski, HulusiOzy, hvtruong, Ian Faust, Imad Saddik, Jérémie du Boisberranger, Jérôme Dockès, John Hendricks, Joris Van den Bossche, Josef Affourtit, Josh, jshn9515, Junaid, KALLA GANASEKHAR, Kapil Parekh, Kenneth Enevoldsen, Kian Eliasi, kostayScr, Krishnan Vignesh, kryggird, Kyle S, Lakshmi Krishnan, Leomax, Loic Esteve, Luca Bittarello, Lucas Colley, Lucy Liu, Luigi Giugliano, Luis, Mahdi Abid, Mahi Dhiman, Maitrey Talware, Mamduh Zabidi, Manikandan Gobalakrishnan, Marc Bresson, Marco Edward Gorelli, Marek Pokropiński, Maren Westermann, Marie Sacksick, Marija Vlajic, Matt J., Mayank Raj, Michael Burkhart, Michael Šimáček, Miguel Fernandes, Miro Hrončok, Mohamed DHIFALLAH, Muhammad Waseem, MUHAMMED SINAN D, Natalia Mokeeva, Nicholas Farr, Nicolas Bolle, Nicolas Hug, nithish-74, Nithurshen, Nitin Pratap Singh, NotAceNinja, Olivier Grisel, omahs, Omar Salman, Patrick Walsh, Peter Holzer, pfolch, ph-ll-pp, Prashant Bansal, Quan H. Nguyen, Radovenchyk, Rafael Ayllón Gavilán, Raghvender, Ranjodh Singh, Ravichandranayakar, Remi Gau, Reshama Shaikh, Richard Harris, RishiP2006, Ritvi Alagusankar, Roberto Mourao, Robert Pollak, Roshangoli, roychan, R Sagar Shresti, Sarthak Puri, saskra, scikit-learn-bot, Scott Huberty, Sercan Turkmen, Sergio P, Shashank S, Shaurya Bisht, Shivam, Shruti Nath, SIKAI ZHANG, sisird864, SiyuJin-1, S. M. Mohiuddin Khan Shiam, Somdutta Banerjee, sotagg, Sota Goto, Spencer Bradkin, Stefan, Stefanie Senger, Steffen Rehberg, Steven Hur, Success Moses, Sylvain Combettes, ThibaultDECO, Thomas J. Fan, Thomas Li, Thomas S., Tim Head, Tingwei Zhu, Tiziano Zito, TJ Norred, Username46786, Utsab Dahal, Vasanth K, Veghit, VirenPassi, Virgil Chan, Vivaan Nanavati, Xiao Yuan, xuzhang0327, Yaroslav Halchenko, Yaswanth Kumar, Zijun yi, zodchi94, Zubair Shakoor