"Wrapper" for feature selection
The feature selection is a crucial aspect of supervised learning process. We must determine the relevant variables for the prediction of the target variable. Indeed, a simpler model is easier to understand and interpret; the deployment will be facilitated, we need less information to collect for prediction; finally, a simpler model is often more robust in generalization i.e. when we want to classify an unseen instance from the population. Three kinds of approaches are often highlighted into the literature. Among them, the WRAPPER approach uses explicitly a performance criterion during the search of the best subset of descriptors. Most often, this is the error rate. But in reality, any kind of criteria can be used. This may be the cost if we use a misclassification cost matrix. It can be the area under curve (AUC) when we assess the classifier using ROC curves, etc. In this case, the learning method is considered as a black box. We try various subsets of predictors. We will choose the o