As stated at the beginning of this section, wrapper methods evaluate subsets of variables to detect the possible interactions between variables being a step ahead of the filter methods.
In wrapper methods, several combinations of variables are used in a predictive model and a score is given to each combination according to the model accuracy.
In wrapper methods, a classifier is iteratively trained with multiple combinations of variables acting as a black box, for which the only output is a ranking of important features.
