Summary

In this chapter, we were introduced to the basic concepts of regression analysis, and then we discovered different types of statistical processes. Starting from the origin of the term regression, we explored the meaning of this type of analysis. We have therefore gone through analyzing the real cases in which it is possible to extract knowledge from the data at our disposal.

Then, we analyzed how to build regression models step by step. Each step of the workflow was analyzed to understand the meaning and the operations to be performed. Particular emphasis was devoted to the generalization ability of the regression model, which is crucial for all other machine learning algorithms. 

We explored the fundamentals of regression analysis and correlation analysis, which allows us to study the relationships between variables. We understood the differences between these two types of analysis.  

In addition, an introduction, background information, and basic knowledge of the R environment were covered. Finally, we explored a number of essential packages that R provides for understanding the amazing world of regression.

In the next chapter, we will approach regression with the simplest algorithm: simple linear regression. First, we will describe a regression problem in terms of where to fit a regressor and then we will provide some intuitions underneath the math formulation. Then, the reader will learn how to tune the model for high performance and deeply understand every parameter of it. Finally, some tricks will be described to lower the complexity and scale the approach.