scikit multilabel classification: ValueError: bad input shape
classification, machine-learning, scikit-learn, stochastic-process
Solution
No, `SGDClassifier` does not do multilabel classification -- it does multiclass classification, which is a different problem, although both are solved using a one-vs-all problem reduction.
Then, neither SGD nor `OneVsRestClassifier.fit` will accept a sparse matrix for `y`. The former wants an array of labels, as you've already found out. The latter wants, for multilabel purposes, a list of lists of labels, e.g.
y = [[1], [2, 3], [1, 3]]
to denote that `X[0]` has label 1, `X[1]` has labels `{2,3}` and `X[2]` has labels `{1,3}`.
Problem
I beieve `SGDClassifier()` with `loss='log'` supports Multilabel classification and I do not have to use OneVsRestClassifier. Check this Now, my dataset is quite big and I am using `HashingVectorizer` and passing result as input to `SGDClassifier`. My target has 42048 features. When I run this, as follows: ``` clf.partial_fit(X_train_batch, y) ``` I get: `ValueError: bad input shape (300000, 42048)`. I have also used classes as the parameter as follows, but still same problem. ``` clf.partial_fit(X_train_batch, y, classes=np.arange(42048)) ``` In the documentation of SGDClassifier, it says `y : numpy array of shape [n_samples]`