Ensemble methods are machine learning techniques that combine multiple models to produce a more accurate and robust predictor. Ensemble methods are widely used in various fields such as finance, healthcare, and social media. These methods have been shown to improve the accuracy and stability of predictions in comparison to using a single model. In this chapter, we will explore the different types of ensemble methods, how they work, and when to use them. We will also discuss some common implementation techniques, and their advantages and disadvantages. Overall, this chapter aims to provide a comprehensive overview of the important aspects of ensemble methods in machine learning.
Some examples of ensemble methods include:
Random forests: This is a popular ensemble method that involves training multiple decision trees and aggregating their predictions to produce a final result.
Boosting: This is a technique that involves training multiple weak models and combining them to produce a strong model. Examples of boosting algorithms include AdaBoost and Gradient Boosting.
Bagging: This is a technique that involves training multiple models on different subsets of the data and combining their predictions to produce a final result. Examples of bagging algorithms include Random Subspace and Pasting.
Stacking: This is a technique that involves training multiple models and combining their predictions as inputs to a meta-model that produces the final result.
Bayesian Model Averaging: This is a technique that involves training multiple models and weighting their predictions according to their likelihood of being correct.
Where to Learn More#
I’ve covered Ensemble Methods in-depth in the following course:
Ensemble Machine Learning in Python: Random Forest, AdaBoost