Supported Algorithms

Applying Intel® Extension for Scikit-learn* impacts the following scikit-learn algorithms:

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 cpp_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 cpp_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, #observations should be >= #features.
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
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
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

See also

oneAPI and GPU support in Intel® Extension for Scikit-learn*

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 cpp_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 cpp_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, #observations should be >= #features.

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

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.

The results of the entire latest scikit-learn test suite with Intel® Extension for Scikit-learn*: CircleCI.