class sklearn.multioutput.MultiOutputRegressor(estimator, n_jobs=None)
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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.
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fit (X, y[, sample_weight]) | Fit the model to data. |
get_params ([deep]) | Get parameters for this estimator. |
partial_fit (X, y[, sample_weight]) | Incrementally fit the model to data. |
predict (X) | Predict multi-output variable using a model trained for each target variable. |
score (X, y[, sample_weight]) | Returns the coefficient of determination R^2 of the prediction. |
set_params (**params) | Set the parameters of this estimator. |
__init__(estimator, n_jobs=None)
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fit(X, y, sample_weight=None)
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Fit the model to data. Fit a separate model for each output variable.
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get_params(deep=True)
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Get parameters for this estimator.
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partial_fit(X, y, sample_weight=None)
[source]
Incrementally fit the model to data. Fit a separate model for each output variable.
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predict(X)
[source]
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score(X, y, sample_weight=None)
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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.
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R^2 is calculated by weighting all the targets equally using multioutput=’uniform_average’
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set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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sklearn.multioutput.MultiOutputRegressor
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html