Summary

In this chapter, we discussed fundamental concepts shared by almost any machine learning model. In the first part, we have introduced the data generating process, as a generalization of a finite dataset. We explained which are the most common strategies to split a finite dataset into a training block and a validation set, and we introduced cross-validation, with some of the most important variants, as one of the best approaches to avoid the limitations of a static split.

In the second part, we discussed the main properties of an estimator: capacity, bias, and variance. We also introduced the Vapnik-Chervonenkis theory, which is a mathematical formalization of the concept of representational capacity, and we analyzed the effects of high biases and high variances. In particular, we discussed effects called underfitting and overfitting, defining the relationship with high bias and high variance.

In the third part, 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. In the last part, we discussed regularization, explaining how it can mitigate the effects of overfitting.

In the next chapter, Chapter 2, Introduction to Semi-Supervised Learning, we're going to introduce semi-supervised learning, focusing our attention on the concepts of transductive and inductive learning.