Predicting multilabel data with sklearn

Your train_test_split() output is not correct. Change this line:

train_x, train_y, test_x, test_y = train_test_split(x, y_enc, test_size=0.33)

To this:

train_x, test_x, train_y, test_y = train_test_split(x, y_enc, test_size=0.33)

Also, to use probabilities instead of class predictions, you'll need to change SVC() to SVC(probability = True) and change clf.predict_proba to clf.predict.

Putting it all together:

from sklearn import metrics
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.cross_validation import train_test_split
from sklearn.svm import SVC


x = [[1,2,3],[3,3,2],[8,8,7],[3,7,1],[4,5,6]]
y = [['bar','foo'],['bar'],['foo'],['foo','jump'],['bar','fox','jump']]

mlb = MultiLabelBinarizer()
y_enc = mlb.fit_transform(y)

train_x, test_x, train_y, test_y = train_test_split(x, y_enc, test_size=0.33)

clf = OneVsRestClassifier(SVC(probability=True))
clf.fit(train_x, train_y)
predictions = clf.predict(test_x)

my_metrics = metrics.classification_report( test_y, predictions)
print my_metrics

This gives me no errors when I run it.

I also experienced "ValueError: Multioutput target data is not supported with label binarization" with OneVsRestClassifier. My issue was caused by the type of training data was "list", after casting with np.array(), it works.