How do Decision Trees perform in real-time prediction systems?

Decision Trees are fast in inference because prediction involves traversing a single path from root to leaf.

In production systems, decision trees are widely used for real-time inference due to their low latency. Prediction involves evaluating a sequence of if-else conditions from root to leaf, typically O(depth). This makes them suitable for high-throughput systems like ad ranking or fraud detection. However, deep trees can increase latency, and large ensembles may require optimization techniques such as model compression or quantization.