The Shooting Regressor; Randomized Gradient-Based Ensembles
Machine Learning
2020-09-15 v1 Machine Learning
Abstract
An ensemble method is introduced that utilizes randomization and loss function gradients to compute a prediction. Multiple weakly-correlated estimators approximate the gradient at randomly sampled points on the error surface and are aggregated into a final solution. A scaling parameter is described that controls a trade-off between ensemble correlation and precision. Numerical methods for estimating optimal values of the parameter are described. Empirical results are computed over a popular dataset. Inferential statistics on these results show that the method is capable of outperforming existing techniques in terms of increased accuracy.
Cite
@article{arxiv.2009.06172,
title = {The Shooting Regressor; Randomized Gradient-Based Ensembles},
author = {Nicholas Smith},
journal= {arXiv preprint arXiv:2009.06172},
year = {2020}
}