As we saw in the previous section, a supervised learning model receives a set of inputs, called features, and maps them to a set of outputs. The assumption is that the information described by the features is useful for determining the value of the corresponding outputs. At first glance, it may seem that the more information we can use as input, the better our chances of predicting the output(s) correctly. However, in many cases, the opposite holds true; if some of the features we use are irrelevant or redundant, the consequence could be a (sometimes significant) decrease in the accuracy of the models.
Feature selection is the process of selecting the most beneficial and essential set of features out of the entire given set of features. Besides increasing the accuracy of the model, a successful feature selection can provide the following...