What is ensemble diversity and why is it important?

Ensemble diversity refers to differences between models in an ensemble, which improves combined performance.

Ensemble learning works best when individual models make uncorrelated errors. Diversity ensures that models do not fail on the same data points. Techniques to increase diversity include using different algorithms, training data subsets, feature sampling, and parameter variation. High diversity combined with reasonably accurate models improves ensemble generalization.