Scikit-learn cross val score: too many indices for array

Question

I have the following code

 from sklearn.ensemble import ExtraTreesClassifier
 from sklearn.cross_validation import cross_val_score
 #split the dataset for tr         


        

Answer 1

When we do cross validation in scikit-learn, the process requires an (R,) shape label instead of (R,1). Although they are the same thing to some extend, their indexing mechanisms are different. So in your case, just add:

c, r = labels.shape
labels = labels.reshape(c,)

before passing it to the cross-validation function.