Hyperparameter Tuning of Machine Learning Models
This chapter describes how genetic algorithms can be used to improve the performance of supervised machine learning models by tuning the hyperparameters of the models. The chapter will start with a brief introduction to hyperparameter tuning in machine learning before describing the concept of a grid search. After introducing the Wine dataset and the adaptive boosting classifier, both of which will be used throughout this chapter, we will demonstrate hyperparameter tuning using both a conventional grid search and a genetic-algorithm-driven grid search. Finally, we will attempt to enhance the results we get by using a direct genetic algorithm approach for hyperparameter tuning.
By the end of this chapter, you will be able to do the following:
- Demonstrate familiarity with the concept of hyperparameter tuning in machine learning
- Demonstrate familiarity with the Wine dataset and the adaptive boosting classifier
- Enhance...
