Robust Loss Functions for Training Decision Trees with Noisy Labels
Robust Loss Functions for Training Decision Trees with Noisy Labels
We consider training decision trees using noisily labeled data, focusing on loss functions that can lead to robust learning algorithms. Our contributions are threefold. First, we offer novel theoretical insights on the robustness of many existing loss functions in the context of decision tree learning. We show that some of …