What is class imbalance impact on Decision Trees?

Class imbalance can bias Decision Trees toward majority classes, reducing minority class detection.

Decision Trees optimize impurity reduction, so they tend to favor splits that improve accuracy on majority classes. In imbalanced datasets, minority classes may be ignored because their contribution to impurity reduction is small. Techniques like class weighting, resampling, or adjusting decision thresholds are needed to mitigate this.