The following code shows an incorrect implementation of Adaboost training algorithm. Please point out all the mistakes you can find and what should be the input variables and output variables of weak_classifier_train. def Adaboost_train(train_data, train_label, T): # train_data: N x d matrix # train_label: N x 1 vector # T: the number of weak classifiers in the ensemble ensemble_models = [] for t in range(0,T): model_param_t = weak_classifier_train(train_data, train_label) # model_param_t returns the model parameters of the learned weak classifier # definition of model ensemble_models.append(model_param_t) return ensemble_models
Added by Juan Luis S.
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The Adaboost algorithm should initialize a weight distribution over the training examples. This is missing in the provided code. We should initialize a weight vector D of size N (number of training examples) with each value as 1/N. Show moreā¦
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