Supported Algorithms

Note

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

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

on CPU

Classification

Algorithm	Parameters	Data formats	Other limitations
`sklearn.svm.SVC`	`kernel` must be one of [`"linear"`, `"rbf"`, `"poly"`, `"sigmoid"`]	Negative weights are not supported.	`probability=True` is not supported with array API classes other than NumPy.
`sklearn.svm.NuSVC`	`kernel` must be one of [`"linear"`, `"rbf"`, `"poly"`, `"sigmoid"`]	Negative weights are not supported.
`sklearn.ensemble.RandomForestClassifier`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'gini'` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.	Number of classes must be at least 2.
`sklearn.ensemble.ExtraTreesClassifier`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'gini'` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.	Number of classes must be at least 2.
`sklearn.neighbors.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'`] `algorithm` == `'ball_tree'` is not supported.	Multi-output and sparse data are not supported.	Number of classes must be at least 2.
`sklearn.linear_model.LogisticRegression`	All parameters are supported except: `solver` not in [`'lbfgs'`, `'newton-cg'`] `l1_ratio` != `0` `dual` = `True` `sample_weight` != `None` `class_weight` != `None` Solver `'newton-cg'` with `fit_intercept` = `False` is not supported	Sparse data is not supported.	Solver `'newton-cg'` is only available in preview mode. Important: this estimator should not be used in parallel Python threads - for concurrent fits (e.g. from `sklearn.model_selection.GridSearchCV`), process-based parallelism should be used instead (default backend for `joblib`).
`sklearn.linear_model.LogisticRegressionCV`	All parameters are supported except: `solver` not in [`'lbfgs'`, `'newton-cg'`] `l1_ratios` not in [`0`, `"warn"`] `dual` = `True` `sample_weight` != `None` `class_weight` != `None` Solver `'newton-cg'` with `fit_intercept` = `False` is not supported	Sparse data is not supported.	Estimator is only available in preview mode.

Regression

Algorithm	Parameters	Data formats
`sklearn.svm.SVR`	`kernel` must be one of [`"linear"`, `"rbf"`, `"poly"`, `"sigmoid"`]	Negative weights are not supported.
`sklearn.svm.NuSVR`	`kernel` must be one of [`"linear"`, `"rbf"`, `"poly"`, `"sigmoid"`]	Negative weights are not supported.
`sklearn.ensemble.RandomForestRegressor`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'squared_error'` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.
`sklearn.ensemble.ExtraTreesRegressor`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'squared_error'` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.
`sklearn.neighbors.KNeighborsRegressor`	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'`] `algorithm` == `'ball_tree'` is not supported.	Multi-output and sparse data are not supported
`sklearn.linear_model.LinearRegression`	All parameters are supported except: `sample_weight` != `None` `positive` = `True` (this is supported through the class `sklearn.linear_model.ElasticNet`)	Only dense data is supported.
`sklearn.linear_model.Ridge`	All parameters are supported except: `solver` != `'auto'` `sample_weight` != `None` `positive` = `True` (this is supported through the class `sklearn.linear_model.ElasticNet`) `alpha` must be a scalar	Only dense data is supported.
`sklearn.linear_model.ElasticNet`	All parameters are supported except: `sample_weight` != `None`	Sparse data is not supported.
`sklearn.linear_model.Lasso`	All parameters are supported except: `sample_weight` != `None`	Sparse data is not supported.

Clustering

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
verbose = True will only print results from the last iteration, and will only print inertia numbers, not ‘convergence achieved’ messages.

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

Dimensionality Reduction

Algorithm

Parameters

Data formats

Other limitations

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.decomposition.IncrementalPCA

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

Estimator is only available in preview mode.

sklearn.manifold.TSNE

All parameters are supported except:

metric != ‘euclidean’ or 'minkowski' with p != 2
n_components can only be 2
method != "barnes_hut"

Refer to TSNE acceleration details to learn more.

Sparse data is not supported for the initialization and distance calculation stages.

Anomaly Detection

Algorithm

Parameters

Data formats

sklearn.neighbors.LocalOutlierFactor

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']

algorithm == 'ball_tree' is not supported.

Sparse data is not supported

Nearest Neighbors

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']

algorithm == 'ball_tree' is not supported.

Sparse data is not supported

Other Tasks

Algorithm	Parameters	Data formats	Other limitations
`sklearn.covariance.EmpiricalCovariance`	All parameters are supported	Only dense data is supported	Estimator is only available in preview mode.
`sklearnex.basic_statistics.BasicStatistics`	All parameters are supported	Supported data formats: Dense data CSR sparse matrices	Sample weights are 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`, `pd.DataFrame` and `pd.Series` inputs are supported.
`sklearn.metrics.pairwise_distances`	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`	Only binary `y_true` is supported

on GPU

Classification

Algorithm	Parameters	Data formats	Other limitations
`sklearn.svm.SVC`	All parameters are supported except: `kernel` not in [`"linear"`, `"rbf"`] `class_weight` != `None` `probability` != `False` (see limitations on this)	Only dense data is supported. Negative weights are not supported.	Only binary classification is supported. If passing `probability=True`, under target_offload, the initial model will be fitted on GPU, but subsequent calibration will be done on CPU through scikit-learn; whereas if passing array API inputs with `probability=True`, the whole procedure will fall back to scikit-learn on CPU.
`sklearn.ensemble.RandomForestClassifier`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'gini'` `oob_score` = `True` `sample_weight` != `None` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.	Number of classes must be at least 2.
`sklearn.ensemble.ExtraTreesClassifier`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'gini'` `oob_score` = `True` `sample_weight` != `None` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.	Number of classes must be at least 2.
`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.	Number of classes must be at least 2. The following methods are not accelerated by Extension for Scikit-learn* and will fall back to scikit-learn on CPU, returning NumPy arrays when using array API inputs: `radius_neighbors()`, `radius_neighbors_graph()`.
`sklearn.linear_model.LogisticRegression`	All parameters are supported except: `solver` != `'newton-cg'` `class_weight` != `None` `sample_weight` != `None` `dual` = `True` `intercept_scaling` != `1` `warm_start` = `True` `l1_ratio` != `0`	No limitations.	Only binary classification is supported.

Regression

Algorithm	Parameters	Data formats
`sklearn.ensemble.RandomForestRegressor`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'squared_error'` `oob_score` = `True` `sample_weight` != `None` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.
`sklearn.ensemble.ExtraTreesRegressor`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'squared_error'` `oob_score` = `True` `sample_weight` != `None` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.
`sklearn.neighbors.KNeighborsRegressor`	All parameters are supported except: `algorithm` != `'brute'` `weights` = `'callable'` `metric` not in [`'euclidean'`, `'manhattan'`, `'minkowski'`, `'chebyshev'`, `'cosine'`]	Only dense data is supported. The following methods are not accelerated by Extension for Scikit-learn* and will fall back to scikit-learn on CPU, returning NumPy arrays when using array API inputs: `radius_neighbors()`, `radius_neighbors_graph()`.
`sklearn.linear_model.Ridge`	All parameters are supported except: `solver` != `'auto'` `sample_weight` != `None` `positive` = `True` `alpha` must be a scalar	Only dense data is supported.
`sklearn.linear_model.LinearRegression`	All parameters are supported except: `sample_weight` != `None` `positive` = `True`	Only dense data is supported.

Clustering

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
verbose = True will only print results from the last iteration, and will only print inertia numbers, not ‘convergence achieved’ messages.

No limitations

sklearn.cluster.DBSCAN

All parameters are supported except:

metric != 'euclidean'
algorithm not in ['brute', 'auto']

Only dense data is supported

Dimensionality Reduction

Algorithm

Parameters

Data formats

Other limitations

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.decomposition.IncrementalPCA

All parameters are supported except:

svd_solver not in ['full', 'covariance_eigh']
For scikit-learn < 1.5: 'full' solver is automatically mapped to 'covariance_eigh'

Sparse data is not supported

Estimator is only available in preview mode.

Anomaly Detection

Algorithm

Parameters

Data formats

Other limitations

sklearn.neighbors.LocalOutlierFactor

All parameters are supported except:

algorithm != 'brute'
metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Only dense data is supported

If using target_offload, some computations outside of neighbor calculations (related to thresholds for outlierness) might happen on CPU.

Nearest Neighbors

Algorithm

Parameters

Data formats

sklearn.neighbors.NearestNeighbors

All parameters are supported except:

algorithm != 'brute'
metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Only dense data is supported. The following methods are not accelerated by Extension for Scikit-learn* and will fall back to scikit-learn on CPU, returning NumPy arrays when using array API inputs: radius_neighbors(), radius_neighbors_graph().

sklearn.neighbors.LocalOutlierFactor

All parameters are supported except:

algorithm != 'brute'
metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Only dense data is supported.

Other Tasks

Algorithm

Parameters

Data formats

Other limitations

sklearn.covariance.EmpiricalCovariance

All parameters are supported

Only dense data is supported

Estimator is only available in preview mode.

sklearnex.basic_statistics.BasicStatistics

All parameters are supported

Supported data formats:

Dense data
CSR sparse matrices

Sample weights are not supported for CSR data format.

SPMD Support

Classification

Algorithm	Parameters & Methods	Data formats	Other limitations
`sklearn.ensemble.RandomForestClassifier`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'gini'` `oob_score` = `True` `sample_weight` != `None` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.	Number of classes must be at least 2.
`sklearn.ensemble.ExtraTreesClassifier`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'gini'` `oob_score` = `True` `sample_weight` != `None` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.	Number of classes must be at least 2.
`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.	Number of classes must be at least 2.
`sklearn.linear_model.LogisticRegression`	All parameters are supported except: `solver` != `'newton-cg'` `class_weight` != `None` `sample_weight` != `None` `dual` = `True` `intercept_scaling` != `1` `warm_start` = `True` `l1_ratio` != `0`	Method `score` is not supported.	Only binary classification is supported

Regression

Algorithm	Parameters & Methods	Data formats
`sklearn.ensemble.RandomForestRegressor`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'squared_error'` `oob_score` = `True` `sample_weight` != `None` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values are not supported.
`sklearn.ensemble.ExtraTreesRegressor`	All parameters are supported except: `warm_start` = `True` `ccp_alpha` != `0` `criterion` != `'squared_error'` `oob_score` = `True` `sample_weight` != `None` `n_estimators` > `6024` `bootstrap` = `True` and/or `max_samples` != `None` are not supported when there are sample weights Non-integer `max_samples` larger than 1 or integer `max_samples` greater than number of rows, with `bootstrap=True`	Multi-output and sparse data are not supported. Missing values and infinite values 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.

Clustering

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
verbose = True will only print results from the last iteration, and will only print inertia numbers, not ‘convergence achieved’ messages.

No limitations

sklearn.cluster.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

Other limitations

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.decomposition.IncrementalPCA

All parameters are supported except:

svd_solver not in ['full', 'covariance_eigh']
For scikit-learn < 1.5: 'full' solver is automatically mapped to 'covariance_eigh'

Sparse data is not supported

Estimator is only available in preview mode.

Nearest Neighbors

Algorithm

Parameters

Data formats

sklearn.neighbors.NearestNeighbors

All parameters are supported except:

algorithm != 'brute'
metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Only dense data is supported

Other Tasks

Algorithm

Parameters

Data formats

Other limitations

sklearn.covariance.EmpiricalCovariance

All parameters are supported

Only dense data is supported

Estimator is only available in preview mode.

sklearnex.basic_statistics.BasicStatistics

All parameters are supported

Supported data formats:

Dense data
CSR sparse matrices

Sample weights not supported for CSR data format

Scikit-learn Tests

Monkey-patched scikit-learn classes and functions passes scikit-learn’s own test suite, with few exceptions - see Scikit-learn’s test suite for details.