Reduce weights for wrongly predicted class
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I'm currently working on a simple prediction system, where the user is asked a series of yes/no questions and based on their responses, a pre-trained model (MLPClassifier) predicted a class and asks the user whether or not the prediction was right. I'm not sure if this is possible, but I was hoping to then alter the weights of the pre-trained model (in sort of an online-learning fashion) so that the network (in that session) doesn't predict the same class later. Currently, I'm just adding the bad responses to a dictionary and if the network predicts the class already in the black-listed set of classes it is ignored...