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• Extension for Scikit-learn*

Supported Algorithms — Extension for Scikit-learn* 2025.7 documentation

Note

To verify that oneDAL is being used for these algorithms, you can enable verbose mode. See verbose mode documentation for details.

Applying Extension for Scikit-learn* impacts the following scikit-learn estimators:

Algorithm

Parameters

Data formats

sklearn.svm.SVR

All parameters are supported

No limitations

sklearn.svm.NuSVR

All parameters are supported

No limitations

sklearn.ensemble.RandomForestRegressor

All parameters are supported except:

• warm_start = True

• ccp_alpha != 0

• criterion != 'mse'

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesRegressor

All parameters are supported except:

• warm_start = True

• ccp_alpha != 0

• criterion != 'mse'

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsRegressor

All parameters are supported except:

• metric != 'euclidean' or 'minkowski' with p != 2

Multi-output and sparse data are not supported

sklearn.linear_model.LinearRegression

All parameters are supported except:

• sample_weight != None

• positive = True

Only dense data is supported.

sklearn.linear_model.Ridge

All parameters are supported except:

• solver != 'auto'

• sample_weight != None

• positive = True

• alpha must be scalar

Only dense data is supported.

sklearn.linear_model.ElasticNet

All parameters are supported except:

• sample_weight != None

Multi-output and sparse data are not supported, #observations should be >= #features.

sklearn.linear_model.Lasso

All parameters are supported except:

• sample_weight != None

Multi-output and sparse data are not supported, #observations should be >= #features.

Algorithm

Parameters

Data formats

sklearn.cluster.KMeans

All parameters are supported except:

• algorithm != 'lloyd' (‘elkan’ falls back to ‘lloyd’)

• n_clusters = 1

• sample_weight must be None, constant, or equal weights

• init = 'k-means++' falls back to CPU

No limitations

sklearn.cluster.DBSCAN

All parameters are supported except:

• metric != 'euclidean' or 'minkowski' with p != 2

• algorithm not in ['brute', 'auto']

Only dense data is supported

Algorithm

Parameters

Data formats

sklearn.decomposition.PCA

All parameters are supported except:

• svd_solver not in ['full', 'covariance_eigh', 'onedal_svd']

• For scikit-learn < 1.5: 'full' solver is automatically mapped to 'covariance_eigh'

Sparse data is not supported

sklearn.manifold.TSNE

All parameters are supported except:

• metric != ‘euclidean’ or 'minkowski' with p != 2

• n_components can only be 2

Refer to TSNE acceleration details to learn more.

Sparse data is not supported

Algorithm

Parameters

Data formats

sklearn.neighbors.NearestNeighbors

• For algorithm == ‘kd_tree’:

  all parameters except metric != 'euclidean' or 'minkowski' with p != 2

• For algorithm == ‘brute’:

  all parameters except metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Sparse data is not supported

Algorithm

Parameters

Data formats

sklearn.covariance.EmpiricalCovariance

All parameters are supported

Only dense data is supported

sklearnex.basic_statistics.BasicStatistics

All parameters are supported

Supported data formats:

• Dense data

• CSR sparse matrices

• Sample weights not supported for CSR data format

sklearn.model_selection.train_test_split

All parameters are supported

Supported data formats:

• Only dense data is supported

• Only integer and 32/64-bits floating point types are supported

• Data with more than 3 dimensions is not supported

• Only np.ndarray inputs are supported.

sklearn.utils.assert_all_finite

All parameters are supported

Only dense data is supported

sklearn.metrics.pairwise_distance

All parameters are supported except:

• metric not in ['cosine', 'correlation']

Supported data formats:

• Only dense data is supported

• Y must be None

• Input dtype must be np.float64

sklearn.metrics.roc_auc_score

All parameters are supported except:

• average != None

• sample_weight != None

• max_fpr != None

• multi_class != None

No limitations

Algorithm

Parameters

Data formats

sklearn.svm.SVC

All parameters are supported except:

• kernel = 'sigmoid_poly'

• class_weight != None

Only binary dense data is supported

sklearn.ensemble.RandomForestClassifier

All parameters are supported except:

• warm_start = True

• ccp_alpha != 0

• criterion != 'gini'

• oob_score = True

• sample_weight != None

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesClassifier

All parameters are supported except:

• warm_start = True

• ccp_alpha != 0

• criterion != 'gini'

• oob_score = True

• sample_weight != None

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsClassifier

All parameters are supported except:

• algorithm != 'brute'

• weights = 'callable'

• metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Only dense data is supported

sklearn.linear_model.LogisticRegression

All parameters are supported except:

• solver != 'newton-cg'

• class_weight != None

• sample_weight != None

• penalty != 'l2'

• dual = True

• intercept_scaling != 1

• warm_start = True

• l1_ratio != 0 and l1_ratio != None

• Only binary classification is supported

No limitations

Algorithm

Parameters

Data formats

sklearn.ensemble.RandomForestRegressor

All parameters are supported except:

• warm_start = True

• ccp_alpha != 0

• criterion != 'mse'

• oob_score = True

• sample_weight != None

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesRegressor

All parameters are supported except:

• warm_start = True

• ccp_alpha != 0

• criterion != 'mse'

• oob_score = True

• sample_weight != None

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsRegressor

All parameters are supported except:

• algorithm != 'brute'

• weights = 'callable'

• metric != 'euclidean' or 'minkowski' with p != 2

Only dense data is supported

sklearn.linear_model.LinearRegression

All parameters are supported except:

• sample_weight != None

• positive = True

Only dense data is supported.

Algorithm

Parameters

Data formats

sklearn.cluster.KMeans

All parameters are supported except:

• algorithm != 'lloyd' (‘elkan’ falls back to ‘lloyd’)

• n_clusters = 1

• sample_weight must be None, constant, or equal weights

• init = 'k-means++' falls back to CPU

No limitations

sklearn.cluster.DBSCAN

All parameters are supported except:

• metric != 'euclidean'

• algorithm not in ['brute', 'auto']

Only dense data is supported

Algorithm

Parameters

Data formats

sklearn.decomposition.PCA

All parameters are supported except:

• svd_solver not in ['full', 'covariance_eigh', 'onedal_svd']

• For scikit-learn < 1.5: 'full' solver is automatically mapped to 'covariance_eigh'

Sparse data is not supported

Algorithm

Parameters

Data formats

sklearn.neighbors.NearestNeighbors

All parameters are supported except:

• algorithm != 'brute'

• weights = 'callable'

• metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Only dense data is supported

Algorithm

Parameters & Methods

Data formats

sklearn.ensemble.RandomForestClassifier

All parameters are supported except:

• warm_start = True

• ccp_alpha != 0

• criterion != 'gini'

• oob_score = True

• sample_weight != None

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesClassifier

All parameters are supported except:

• warm_start = True

• ccp_alpha != 0

• criterion != 'gini'

• oob_score = True

• sample_weight != None

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsClassifier

All parameters are supported except:

• algorithm != 'brute'

• weights = 'callable'

• metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

• predict_proba method not supported

Only dense data is supported

sklearn.linear_model.LogisticRegression

All parameters are supported except:

• solver != 'newton-cg'

• class_weight != None

• sample_weight != None

• penalty != 'l2'

• dual = True

• intercept_scaling != 1

• multi_class != 'multinomial'

• warm_start = True

• l1_ratio != None

• Only binary classification is supported

No limitations

Algorithm

Parameters & Methods

Data formats

sklearn.ensemble.RandomForestRegressor

All parameters are supported except:

• warm_start = True

• ccp_alpha != 0

• criterion != 'mse'

• oob_score = True

• sample_weight != None

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesRegressor

All parameters are supported except:

• warm_start = True

• ccp_alpha != 0

• criterion != 'mse'

• oob_score = True

• sample_weight != None

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsRegressor

All parameters are supported except:

• algorithm != 'brute'

• weights = 'callable'

• metric != 'euclidean' or 'minkowski' with p != 2

Only dense data is supported

sklearn.linear_model.LinearRegression

All parameters are supported except:

• sample_weight != None

• positive = True

Only dense data is supported.

Algorithm

Parameters & Methods

Data formats

sklearn.cluster.KMeans

All parameters are supported except:

• algorithm != 'lloyd' (‘elkan’ falls back to ‘lloyd’)

• n_clusters = 1

• sample_weight must be None, constant, or equal weights

• init = 'k-means++' falls back to CPU

No limitations

sklearn.cluster.DBSCAN

All parameters are supported except:

• metric != 'euclidean'

• algorithm not in ['brute', 'auto']

Only dense data is supported

Algorithm

Parameters & Methods

Data formats

sklearn.decomposition.PCA

All parameters are supported except:

• svd_solver not in ['full', 'covariance_eigh', 'onedal_svd']

• For scikit-learn < 1.5: 'full' solver is automatically mapped to 'covariance_eigh'

Sparse data is not supported

Algorithm

Parameters

Data formats

sklearn.neighbors.NearestNeighbors

All parameters are supported except:

• algorithm != 'brute'

• weights = 'callable'

• metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Only dense data is supported

Monkey-patched scikit-learn classes and functions passes scikit-learn’s own test suite, with few exceptions, specified in deselected_tests.yaml.

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