River2SKLRegressor¶
Compatibility layer from River to scikit-learn for regression.
Parameters¶
- river_estimator (base.Regressor)
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
Returns
self
predict
Predicts the target of an entire dataset contained in memory.
Parameters
- X
Returns
ndarray: Predicted target values for each row of X
.
score
Return the coefficient of determination of the prediction.
The coefficient of determination :math:R^2
is defined as :math:(1 - \frac{u}{v})
, where :math:u
is the residual sum of squares ((y_true - y_pred)** 2).sum()
and :math:v
is the total sum of squares ((y_true - y_true.mean()) ** 2).sum()
. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y
, disregarding the input features, would get a :math:R^2
score of 0.0.
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