Hi all.
A quick questions on future API.
What should happen if a user passes an X with shape (N,), in other words
X.ndim == 1?
This is unfortunately not really consistent in scikit-learn right now.
Three things are possible:
1) Raise an error
2) N = n_features, that is X contains a single sample
3) N = n_samples, that is X has a single feature
I would think it should be N=n_samples. Gael thinks (iirc) we should
raise an error.
In the code, we currently take N=n_features in predict,
decision_function, predict_proba and transform, basically everywhere.
This is in part due to using ``check_array`` everywhere, which used the
backward-compatible (but odd) behavior of np.atleast2d.
In ``fit``it looks like all estimators assume |N=n_features|, apart from
DictionaryLearning, MinMaxScaler, StandardScaler, which assume
|N=n_samples.|
See https://github.com/scikit-learn/scikit-learn/pull/4511 for more
discussion
Obviously any change we make would mean a deprecation cycle, which will
mean warning in 0.17 and 0.18 when someone gives a 1-dim X that we'll
change something soon, and then actually change it in 0.19 (1.0?).
Andy
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