Summary

In this chapter, we have learned about the different types of features that are generated to train a model. We have derived domain-specific features and datatype-specific features. Also, we explored an automated technique for generating text features. The feature engineering process is essential for obtaining the best model performance. We delved into two variable transformation techniques and learned about techniques to identify redundant features and handle them in a dataset.

We have learned about forward and backward feature selection approaches and have performed correlation analysis through detailed examples. We implemented the calculation of p-values in R and looked at its significance to the process of the selection of features. Recursive feature elimination is another way that we saw to find the best combination of features for a model. We delved into a dimensionality reduction approach, known as PCA, that drastically reduces the number of features needed, as it calculates...