Ensembling with dynamic weights
I thing Voting Classifier only accepts different static weights for each estimator. However you may solve the problem by assigning class weights with the class_weight parameter of the random forest estimator by calculating the class weights on your train set.
I was wondering if it is possible to use dynamic weights in sklearn's VotingClassifier. Overall i have 3 labels 0 = Other, 1 = Spam, 2 = Emotion. By dynamic weights I mean the following:
I have 2 classifiers. First one is a Random Forest which performs best on Spam detection. Other one is a CNN which is superior for topic detection (good distinction between Other and Emotion). What I would like is a VotingClassifier that gives a higher weight to RF when it assigns the label "Spam/1".
Is VotingClassifier the right way to go?
Best regards,
Stefan