Supported Algorithms — Extension for Scikit-learn* 2025.4 documentation (original) (raw)

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

on CPU

Classification

Algorithm Parameters Data formats
SVC All parameters are supported No limitations
NuSVC All parameters are supported No limitations
RandomForestClassifier All parameters are supported except: warm_start = True ccp_alpha != 0 criterion != ‘gini’ Multi-output and sparse data are not supported
KNeighborsClassifier 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’] Multi-output and sparse data are not supported
LogisticRegression All parameters are supported except: solver not in [‘lbfgs’, ‘newton-cg’] class_weight != None sample_weight != None Only dense data is supported

Regression

Algorithm Parameters Data formats
SVR All parameters are supported No limitations
NuSVR All parameters are supported No limitations
RandomForestRegressor All parameters are supported except: warm_start = True ccp_alpha != 0 criterion != ‘mse’ Multi-output and sparse data are not supported
KNeighborsRegressor All parameters are supported except: metric != ‘euclidean’ or ‘minkowski’ with p != 2 Multi-output and sparse data are not supported
LinearRegression All parameters are supported except: normalize != False sample_weight != None Only dense data is supported.
Ridge All parameters are supported except: normalize != False solver != ‘auto’ sample_weight != None Only dense data is supported, #observations should be >= #features.
ElasticNet All parameters are supported except: sample_weight != None Multi-output and sparse data are not supported, #observations should be >= #features.
Lasso All parameters are supported except: sample_weight != None Multi-output and sparse data are not supported, #observations should be >= #features.

Clustering

Algorithm Parameters Data formats
KMeans All parameters are supported except: precompute_distances sample_weight != None No limitations
DBSCAN All parameters are supported except: metric != ‘euclidean’ or ‘minkowski’ with p != 2 algorithm not in [‘brute’, ‘auto’] Only dense data is supported

Dimensionality Reduction

Algorithm Parameters Data formats
PCA All parameters are supported except: svd_solver not in [‘full’, ‘covariance_eigh’] Sparse data is not supported
IncrementalPCA All parameters are supported Sparse data is not supported
TSNE All parameters are supported except: metric != ‘euclidean’ or ‘minkowski’ with p != 2 Refer to TSNE acceleration details to learn more. Sparse data is not supported

Nearest Neighbors

Algorithm Parameters Data formats
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

Other Tasks

Algorithm Parameters Data formats
EmpiricalCovariance All parameters are supported Only dense data is supported
train_test_split All parameters are supported Only dense data is supported
assert_all_finite All parameters are supported Only dense data is supported
pairwise_distance All parameters are supported except: metric not in [‘cosine’, ‘correlation’] Only dense data is supported
roc_auc_score All parameters are supported except: average != None sample_weight != None max_fpr != None multi_class != None No limitations

on GPU

Classification

Algorithm Parameters Data formats
SVC All parameters are supported except: kernel = ‘sigmoid_poly’ class_weight != None Only binary dense data is supported
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
KNeighborsClassifier All parameters are supported except: algorithm != ‘brute’ weights = ‘callable’ metric not in [‘euclidean’, ‘manhattan’, ‘minkowski’, ‘chebyshev’, ‘cosine’] Only dense data is supported
LogisticRegression All parameters are supported except: solver != ‘newton-cg’ class_weight != None sample_weight != None penalty != ‘l2’ Only dense data is supported

Regression

Algorithm Parameters Data formats
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
KNeighborsRegressor All parameters are supported except: algorithm != ‘brute’ weights = ‘callable’ metric != ‘euclidean’ or ‘minkowski’ with p != 2 Only dense data is supported
LinearRegression All parameters are supported except: normalize != False sample_weight != None Only dense data is supported.

Clustering

Algorithm Parameters Data formats
KMeans All parameters are supported except: precompute_distances sample_weight != None Init = ‘k-means++’ fallbacks to CPU. Sparse data is not supported
DBSCAN All parameters are supported except: metric != ‘euclidean’ algorithm not in [‘brute’, ‘auto’] Only dense data is supported

Dimensionality Reduction

Algorithm Parameters Data formats
PCA All parameters are supported except: svd_solver not in [‘full’, ‘covariance_eigh’] Sparse data is not supported

Nearest Neighbors

Algorithm Parameters Data formats
NearestNeighbors All parameters are supported except: algorithm != ‘brute’ weights = ‘callable’ metric not in [‘euclidean’, ‘manhattan’, ‘minkowski’, ‘chebyshev’, ‘cosine’] Only dense data is supported

Other Tasks

Algorithm Parameters Data formats
EmpiricalCovariance All parameters are supported Only dense data is supported

SPMD Support

Classification

Algorithm Parameters & Methods Data formats
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
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
LogisticRegression All parameters are supported except: solver != ‘newton-cg’ class_weight != None sample_weight != None penalty != ‘l2’ Only dense data is supported

Regression

Algorithm Parameters & Methods Data formats
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
KNeighborsRegressor All parameters are supported except: algorithm != ‘brute’ weights = ‘callable’ metric != ‘euclidean’ or ‘minkowski’ with p != 2 Only dense data is supported
LinearRegression All parameters are supported except: normalize != False sample_weight != None Only dense data is supported.

Clustering

Algorithm Parameters & Methods Data formats
KMeans All parameters are supported except: precompute_distances sample_weight != None Init = ‘k-means++’ fallbacks to CPU. Sparse data is not supported
DBSCAN All parameters are supported except: metric != ‘euclidean’ algorithm not in [‘brute’, ‘auto’] Only dense data is supported

Dimensionality Reduction

Algorithm Parameters & Methods Data formats
PCA All parameters are supported except: svd_solver not in [‘full’, ‘covariance_eigh’] fit is the only method supported Sparse data is not supported

Nearest Neighbors

Algorithm Parameters Data formats
NearestNeighbors All parameters are supported except: algorithm != ‘brute’ weights = ‘callable’ metric not in [‘euclidean’, ‘manhattan’, ‘minkowski’, ‘chebyshev’, ‘cosine’] Only dense data is supported

Other Tasks

Algorithm Parameters Data formats
EmpiricalCovariance All parameters are supported Only dense data is supported

Scikit-learn Tests

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