Tuning the hyperparameters using conventional versus genetic grid search

To encapsulate the hyperparameter tuning of the AdaBoost classifier for the Wine dataset using a grid search—both the conventional version and the genetic-algorithm-driven version—we created a Python class called HyperparameterTuningGrid. This class can be found in the 01_hyperparameter_tuning_grid.py file, which is located at

https://github.com/PacktPublishing/Hands-On-Genetic-Algorithms-with-Python-Second-Edition/blob/main/chapter_08/01_hyperparameter_tuning_grid.py.

The main parts of this class are highlighted as follows:

1. The __init__() method of the class initializes the wine dataset, the AdaBoost classifier, the k-fold cross-validation metric, and the grid parameters:

   self.initWineDataset()
   self.initClassifier()
   self.initKfold()
   self.initGridParams()

2. The initGridParams() method initializes the grid search by setting the tested values of the three hyperparameters mentioned in...