A Gentle Introduction to Ensemble Diversity for Machine Learning
Ensemble learning combines the predictions from machine learning models for classification and regression.
We pursue using ensemble methods to achieve improved predictive performance, and it is this improvement over any of the contributing models that defines whether an ensemble is good or not.
A property that is present in a good ensemble is the diversity of the predictions made by contributing models. Diversity is a slippery concept as it has not been precisely defined; nevertheless, it provides a useful practical heuristic for designing good ensemble models.
In this post, you will discover ensemble diversity in machine learning.
After reading this post, you will know:
- A good ensemble is one that has better performance than any contributing model.
- Ensemble diversity