A Provably Accurate Randomized Sampling Algorithm for Logistic Regression

arXiv:2402.16326v1 Announce Type: cross
Abstract: In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model. Our analysis builds upon two simple structural conditions that boil down to randomized matrix multiplication, a fundamental …

abstract algorithm arxiv binary classification cs.ds cs.lg logistic regression machine machine learning quality regression sampling simple statistics stat.ml supervised learning tasks type variables