skmultiflow.core.RegressorMixin

class skmultiflow.core.RegressorMixin

Mixin class for all regression estimators in scikit-multiflow.

Methods

fit(self, X, y[, sample_weight])	Fit the model.
partial_fit(self, X, y[, sample_weight])	Partially (incrementally) fit the model.
predict(self, X)	Predict target values for the passed data.
predict_proba(self, X)	Estimates the probability for probabilistic/bayesian regressors
score(self, X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.

fit(self, X, y, sample_weight=None)

Fit the model.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The features to train the model.

y: numpy.ndarray of shape (n_samples, n_targets)

An array-like with the target values of all samples in X.

sample_weight: numpy.ndarray, optional (default=None)

Samples weight. If not provided, uniform weights are assumed. Usage varies depending on the learning method.

Returns

self

abstract partial_fit(self, X, y, sample_weight=None)

Partially (incrementally) fit the model.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The features to train the model.

y: numpy.ndarray of shape (n_samples)

An array-like with the target values of all samples in X.

sample_weight: numpy.ndarray of shape (n_samples), optional (default=None)

Samples weight. If not provided, uniform weights are assumed. Usage varies depending on the learning method.

Returns

self

abstract predict(self, X)

Predict target values for the passed data.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The set of data samples to predict the target values for.

Returns

A numpy.ndarray with all the predictions for the samples in X.

abstract predict_proba(self, X)

Estimates the probability for probabilistic/bayesian regressors

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The matrix of samples one wants to predict the probabilities for.

Returns

numpy.ndarray

score(self, X, y, sample_weight=None)

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and 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 R^2 score of 0.0.

Parameters

Xarray-like, shape = (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like, shape = (n_samples) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like, shape = [n_samples], optional

Sample weights.

Returns

scorefloat

R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with metrics.r2_score. This will influence the score method of all the multioutput regressors (except for multioutput.MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call metrics.r2_score directly or make a custom scorer with metrics.make_scorer (the built-in scorer 'r2' uses multioutput='uniform_average').