Summary
In this chapter, we learned the basic concepts of multiple linear regression, where linear regression is extended to extract predictive information from more than one feature. We saw how to tune the multiple linear regression model for higher performance and deeply understood every parameter of it. We understood the information contained in linear regression models that we can build with the lm function. Furthermore, we have learned to carry out a proper residuals analysis to understand, in depth, whether the model we built has been effective in predicting our system. We dealt with the case of a linear regression model with categorical variables.
We then explored the SGD technique for optimization of algorithms used on regression to find a good set of model parameters given a training dataset. After analyzing the GD algorithms in detail, we solved a multiple linear regression problem with the use of the sgd package.
Finally, polynomial regression was introduced where linear regression...
