How do Decision Trees behave in high-dimensional feature spaces?

Decision Trees can struggle in high-dimensional spaces due to sparse useful splits and increased risk of overfitting.

In high-dimensional datasets, many features may be irrelevant, making it harder for greedy splitting to find meaningful partitions. Trees may overfit noise or select weak splits early that distort the structure. Feature selection, dimensionality reduction, or ensemble methods like Random Forests and Gradient Boosting are typically used to improve performance.