Summary
In this chapter, we introduced the loss and cost functions, first as proxies of the expected risk, and then we detailed some common situations that can be experienced during an optimization problem. We also exposed some common cost functions, together with their main features and specific applications.
In the last part, we discussed regularization, explaining how it can mitigate the effects of overfitting and induce sparsity. In particular, the employment of Lasso can help the data scientist to perform automatic feature selection by forcing all secondary coefficients to become equal to 0.
In the next chapter, Chapter 3, Introduction to Semi-Supervised Learning, we're going to introduce semi-supervised learning, focusing our attention on the concepts of transductive and inductive learning.
