In recent years, big data and machine learning have become increasingly popular in many areas. It is generally believed that the greater the number of variables there are, the more accurate a classifier becomes. However, this is not always true.
In this chapter, we will reduce the number of variables in the dataset by analyzing the individual predictive power of each variable and using different alternatives.
In this chapter, we will cover the following topics:
- Feature selection algorithm
- Filter method
- Wrapper method
- Embedded methods
- Dimensionality reduction
