What is ensemble learning for high-dimensional data problems?

Ensemble learning handles high-dimensional data by reducing variance and selecting informative feature subsets.

High-dimensional datasets suffer from sparsity and overfitting. Ensemble methods like Random Forest, Gradient Boosting, and Extra Trees reduce dimensionality issues by selecting random feature subsets at each split or model. This introduces implicit feature selection and reduces variance. Stacking with dimensionality reduction techniques like PCA can further improve robustness. Ensembles are especially effective when features are redundant or highly correlated.