River2SKLClassifier¶
Compatibility layer from River to scikit-learn for classification.
Parameters¶
- river_estimator (base.Classifier)
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