The Practical Importance of Feature Selection

If you wanted to classify animals, for example, based on a plethora of relevant collected data, you would quickly find that all sorts of potential data attributes, or features, were relatively unhelpful for classification. For example, given that most living creatures have precisely 1 heart, this particular feature would not be beneficial, from a learning perspective. On the other hand, an attribute denoting whether or not a given animal is hoofed would likely be a powerful predictor. Further, using all of these irrelevant attributes, mixed in with the powerful predictors, may actually have a negative effect on the resulting model. This is to say nothing of the increased training times that may come along with the inclusion of useless attributes, or the overfitting which may occur on the training data.