Hi All,
I've tried GridsearchCV with RandomForestClassifier() clf = GridSearchCV(RandomForestClassifier(), tuned_parameters, cv=5, scoring='accuracy') for a multi-label problem where the output is a list of lists of 20 zeros or ones. [[1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0], [1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1],... Even though it was done correctly in usual way with clf = RandomForestClassifier(max_depth=7, random_state=0) clf.fit(Xtr,y) with GridsearchCV I have the errors below: 83 # We can't have more than one value on y_type => The set is no more needed ValueError: Classification metrics can't handle a mix of multiclass-multioutput and multilabel-indicator targets Is it possible to perform GridsearchCV in scikit for the multilabel setting (with an appropriate metric like averaged zero-one-loss)? Any hints? Thank you and best regards, Dmitry
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
