# River2SKLClassifier¶

Compatibility layer from River to scikit-learn for classification.

## Methods¶

fit

Fits to an entire dataset contained in memory.

Parameters

• X
• y

Returns

self

get_params

Get parameters for this estimator.

Parameters

• deep – defaults to True

Returns

dict

partial_fit

Fits incrementally on a portion of a dataset.

Parameters

• X
• y
• classes – defaults to None

Returns

self

predict

Predicts the target of an entire dataset contained in memory.

Parameters

• X

Returns

Predicted target values for each row of X.

predict_proba

Predicts the target probability of an entire dataset contained in memory.

Parameters

• X

Returns

Predicted target values for each row of X.

score

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

• X
• y
• sample_weight – defaults to None

Returns

float

set_params

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as :class:~sklearn.pipeline.Pipeline). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Parameters

• params

Returns

estimator instance