In regression, multicollinearity between variables would need to be removed to suit the model assumption. In building a stacked ensemble model, with say SVM, xgb and a decision tree as a base model with logistic regression as a meta learner, is it necessary to ensure that variables selected for SVM, xgb and decision tree models would need to be free from multicollinearity?



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