Enhancing Machine Learning Models Using Feature Selection
This chapter describes how genetic algorithms can be used to improve the performance of supervised machine learning models by selecting the best subset of features from the provided input data. We will start with a brief introduction to machine learning and then describe the two main types of supervised machine learning tasks – regression and classification. We will then discuss the potential benefits of feature selection when it comes to the performance of these models. Next, we will demonstrate how genetic algorithms can be utilized to pinpoint the genuine features that are generated by the Friedman-1 Test regression problem. Then, we will use the real-life Zoo dataset to create a classification model and improve its accuracy – again by applying genetic algorithms to isolate the best features for the task.
In this chapter, we will cover the following topics:
- Understand the basic concepts of supervised...
