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
