We're halfway through our text and we have gotten our hands dirty with about a dozen datasets and have seen a great deal of feature selection methods that we, as data scientists and machine learning engineers, may utilize in our work and lives to ensure that we are getting the most out of our predictive modeling. So far, in dealing with data, we have worked with methods including:
- Feature understanding through the identification of levels of data
- Feature improvements and imputing missing values
- Feature standardization and normalization
Each of the preceding methods has a place in our data pipeline and, more often than not, two or more methods are used in tandem with one another.
The remainder of this text will focus on other methods of feature engineering that are, by nature, a bit more mathematical and complex than in the first half of this book...
