Ensemble models
Ensemble modeling is a technique in machine learning that combines the predictions of multiple models to improve overall performance. The idea behind ensemble models is that multiple models can be better than a single model as different models may capture different patterns in the data.
There are several types of ensemble models, all of which we’ll cover in the following sections.
Bagging
Bootstrap aggregating, also known as bagging, is an ensemble method that combines multiple independent models trained on different subsets of the training data to reduce variance and improve model generalization.
The bagging algorithm can be summarized as follows:
- Given a training dataset of size n, create m bootstrap samples of size n (that is, sample n instances with replacement m times).
- Train a base model (for example, a decision tree) on each bootstrap sample independently.
- Aggregate the predictions of all base models to obtain the ensemble prediction...
