Hands-On Genetic Algorithms with Python

The adaptive boosting classifier

The adaptive boosting algorithm, or AdaBoost, for short, is a powerful machine learning model that combines the outputs of multiple instances of a simple learning algorithm (weak learner) using a weighted sum. AdaBoost adds instances of the weak learner during the learning process, each of which is adjusted to improve previously misclassified inputs.

The sklearn library's implementation of this model, AdaboostClassifier (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html), uses several hyperparameters, some of which are as follows:

Name	Type	Description	Default Value
n_estimators	int	The maximum number of estimators	50
learning_rate	float	Can be used to shrink ...