Not a member yet?
Join now and access exclusive content.


It is a technique in machine learning that improves predictive performance by combining multiple individual models. These models can be of the same type or different types. By merging the predictions of several models, ensemble methods can reduce bias and variance, resulting in a more robust and generalizable model. Additionally, it can mitigate the individual errors of each model. Common ensemble methods include bagging, boosting, and model voting combinations.

The use of ensembles can lead to significant improvements in the accuracy and robustness of machine learning models, especially in situations where individual models have limitations or exhibit high variance.

Sign up for the Newsletter
Thank you for subscribing to our newsletter!