Yes, some can use a shared model to predict multiple outputs (ElasticNet, DecisionTreeRegressor, MLPRegressor), others can't. Those that can't can be trivially extended to the multiple output case with MultiOutputRegressor, by learning each output independently.
On Tue, 11 Dec 2018 at 20:11, lampahome <pahome.c...@mirlab.org> wrote: > As title, apart from sklearn.multioutput.MultiOutputRegressor, almost > regression algo in sklearn only can predict 1-d output. > > Ex: predict 1-d output > sklearn.linear_model.SGDRegressor > fit(X, y, coef_init=None, intercept_init=None, sample_weight=None) > y : numpy array, shape (n_samples,) > > Ex: predict multiple output > sklearn.linear_model.ElasticNet > fit(X, y, check_input=True) > y : ndarray, shape (n_samples,) or (n_samples, n_targets) > > There're two kind of output for regression methods. > > What's the difference? > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
