Supported algorithms

Applying Intel(R) Extension for Scikit-learn will impact the following existing scikit-learn algorithms:

Task

Functionality

Parameters support

Data support

Classification

SVC

All parameters except kernel = ‘sigmoid’.

No limitations.

Classification

NuSVC

All parameters except kernel = ‘sigmoid’.

No limitations.

Classification

RandomForestClassifier

All parameters except warm_start = True, cpp_alpha != 0, criterion != ‘gini’, oob_score = True.

Multi-output, sparse data and out-of-bag score are not supported.

Classification

KNeighborsClassifier

All parameters except metric != ‘euclidean’ or minkowski with p != 2.

Multi-output and sparse data is not supported.

Classification

LogisticRegression

All parameters except solver != ‘lbfgs’ or ‘newton-cg’, class_weight != None, sample_weight != None.

Only dense data is supported.

Regression

SVR

All parameters except kernel = ‘sigmoid’.

No limitations.

Regression

NuSVR

All parameters except kernel = ‘sigmoid’.

No limitations.

Regression

RandomForestRegressor

All parameters except warm_start = True, cpp_alpha != 0, criterion != ‘mse’, oob_score = True.

Multi-output, sparse data and out-of-bag score are not supported.

Regression

KNeighborsRegressor

All parameters except metric != ‘euclidean’ or ‘minkowski’ with p != 2.

Multi-output and sparse data is not supported.

Regression

LinearRegression

All parameters except normalize != False and sample_weight != None.

Only dense data is supported, #observations should be >= #features.

Regression

Ridge

All parameters except normalize != False, solver != ‘auto’ and sample_weight != None.

Only dense data is supported, #observations should be >= #features.

Regression

ElasticNet

All parameters except sample_weight != None.

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

Regression

Lasso

All parameters except sample_weight != None.

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

Clustering

KMeans

All parameters except precompute_distances and sample_weight != None.

No limitations.

Clustering

DBSCAN

All parameters except metric != ‘euclidean’ or ‘minkowski’ with p != 2, algorithm != ‘brute’ or ‘auto’.

Only dense data is supported.

Dimensionality reduction

PCA

All parameters except svd_solver != ‘full’.

Sparse data is not supported.

Dimensionality reduction

TSNE

All parameters except metric != ‘euclidean’ or ‘minkowski’ with p != 2.

Sparse data is not supported.

Unsupervised

NearestNeighbors

All parameters except metric != ‘euclidean’ or ‘minkowski’ with p != 2.

Sparse data is not supported.

Other

train_test_split

All parameters are supported.

Only dense data is supported.

Other

assert_all_finite

All parameters are supported.

Only dense data is supported.

Other

pairwise_distance

With metric = ‘cosine’ or ‘correlation’.

Only dense data is supported.

Other

roc_auc_score

Parameters average, sample_weight, max_fpr and multi_class are not supported.

No limitations.

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(R) Extension for Scikit-learn: CircleCI.