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I'm using GradientBoostingClassifier for my unbalanced labeled datasets. It seems like class weight doesn't exist as a parameter for this classifier in Sklearn. I see I can use sample_weight when fit but I cannot use it when I deal with VotingClassifier or GridSearch. Could someone help?
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If 'balanced', class weights will be given by n_samples / (n_classes * np. bincount(y)) . If a dictionary is given, keys are classes and values are corresponding class weights. If None is given, the class weights will be uniform.
Generating class weights In binary classification, class weights could be represented just by calculating the frequency of the positive and negative class and then inverting it so that when multiplied to the class loss, the underrepresented class has a much higher error than the majority class.
The class_weight is a dictionary that defines each class label (e.g. 0 and 1) and the weighting to apply in the calculation of group purity for splits in the decision tree when fitting the model.
Logistic Regression (manual class weights): The idea is, if we are giving n as the weight for the minority class, the majority class will get 1-n as the weights. Here, the magnitude of the weights is not very large but the ratio of weights between majority and minority class will be very high.
Currently there isn't a way to use class_weights for GB in sklearn.
Don't confuse this with sample_weight
Sample Weights change the loss function and your score that you're trying to optimize. This is often used in case of survey data where sampling approaches have gaps.
Class Weights are used to correct class imbalances as a proxy for over \ undersampling. There is no direct way to do that for GB in sklearn (you can do that in Random Forests though)
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