Mastering Machine Learning Algorithms

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 ...