Topological Forest

Abstract

We propose a new ML model called Topological Forest that contains an ensemble of decision trees. Unlike a vanilla Random Forest, Topological Forest has a special training process that selects a smaller number of decision trees on a topological graph representation that TDA Mapper constructs. Compared to Vanilla Random Forest, Topological Forest significantly improves the computational efficiency of inference time due to the smaller ensemble size and selection of better decision trees while keeping the diversity of decision trees. Our experiments show that Topological Forest can speed up inference time by more than 100x on average while compromising at most 2% reduction in the AUC metric for the prediction quality.

Comparative Analysis of SVM and RF Algorithms for Tsunami Prediction: A Performance Evaluation Study

“…The algorithm has several hyperparameters that need to be set by the user, such as the number of observations drawn randomly for each tree, the number of variables drawn randomly for each split, and the minimum number of samples that a node must contain [24].…”