What is the role of decision boundary fragmentation in Random Forest?

Random Forest creates fragmented decision boundaries that collectively approximate smooth functions.

Each tree partitions space into rectangular regions. When aggregated, overlapping partitions create a fragmented but statistically smooth boundary. This fragmentation reduces variance and increases flexibility, allowing RF to approximate highly nonlinear functions without explicit parametric modeling.