# MultiOutputRegressor¶

class ibex.sklearn.multioutput.MultiOutputRegressor(estimator, n_jobs=1)

Bases: sklearn.multioutput.MultiOutputRegressor, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Multi target regression

This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.

estimator : estimator object
An estimator object implementing fit and predict.
n_jobs : int, optional, default=1
The number of jobs to run in parallel for fit. If -1, then the number of jobs is set to the number of cores. When individual estimators are fast to train or predict using n_jobs>1 can result in slower performance due to the overhead of spawning processes.
fit(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the model to data.

Fit a separate model for each output variable.

X : (sparse) array-like, shape (n_samples, n_features)
Data.
y : (sparse) array-like, shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel estimation.
sample_weight : array-like, shape = (n_samples) or None
Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
self : object
Returns self.
partial_fit(X, y, sample_weight=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Incrementally fit the model to data.

Fit a separate model for each output variable.

X : (sparse) array-like, shape (n_samples, n_features)
Data.
y : (sparse) array-like, shape (n_samples, n_outputs)
Multi-output targets.
sample_weight : array-like, shape = (n_samples) or None
Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
self : object
Returns self.
predict(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict multi-output variable using a model
trained for each target variable.
X : (sparse) array-like, shape (n_samples, n_features)
Data.
y : (sparse) array-like, shape (n_samples, n_outputs)
Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.
score(X, y, sample_weight=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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 regression sum of squares ((y_true - y_true.mean()) ** 2).sum(). 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.

R^2 is calculated by weighting all the targets equally using multioutput=’uniform_average’.

X : array-like, shape (n_samples, n_features)
Test samples.
y : array-like, shape (n_samples) or (n_samples, n_outputs)
True values for X.
sample_weight : array-like, shape [n_samples], optional
Sample weights.
score : float
R^2 of self.predict(X) wrt. y.