Supervised Learning

In this chapter, we introduce the basics of classification: a type of supervised machine learning. We also give a brief practical tour of learning theory and good practices for successful use of classifiers in a real case using Python. The chapter starts by introducing the classic machine learning pipeline, defining features, and evaluating the performance of a classifier. After that, the notion of generalization error is needed, which allows us to show learning curves in terms of the number of examples and the complexity of the classifier, and also to define the notion of overfitting. That notion will then allow us to develop a strategy for model selection. Finally, two of the best-known techniques in machine learning are introduced: support vector machines and random forests. These are then applied to the proposed problem of predicting those loans that will not be successfully covered once they have been accepted.