Boosting algorithms and Naive Bayes
Boosting is a machine learning technique that involves creating an ensemble of weak learners to form a strong learner. The idea behind boosting algorithms is to iteratively train models on the data, where each new model attempts to correct the errors of the previous model. Boosting algorithms are widely used in supervised learning tasks, such as classification and regression.
There are several key types of boosting algorithms:
- AdaBoost (Adaptive Boosting): AdaBoost is one of the earliest and most popular boosting algorithms. It starts by training a base classifier on the entire dataset and then sequentially trains additional classifiers on the samples that the previous classifiers got wrong. The final prediction is made by taking a weighted sum of the predictions of all the classifiers.
- Gradient Boosting: Gradient Boosting is another popular boosting algorithm that works by iteratively adding new models to the ensemble, each trained...
