[scikit-learn] Why some regression algo can predict multiple out?

[lampahome]

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?
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