Year: 2025
Type: article
Abstract: In ensemble machine learning, we combine the decisions of weak learners to derive a decision that is, hopefully, better than the individual ones. The combination of these learners can be aggregated by a majority vote or simple averaging, or it can be more complicated and involve multiple steps such as in boosting. In this paper, we consider the question of predicting the accuracy of an ensemble created with bagging for a given number of weak learners. We achieve a low relative error on our predictions and can make this prediction in a shorter time, as compared to training the ensemble over various sizes.
Authors: Eugene Pinsky, Siddhant Shah
Institution: Boston University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408677139