Performance of Regression Models as a Function of Experiment Noise.

ABSTRACT:

Background

A challenge in developing machine learning regression models is that it is difficult to know whether maximal performance has been reached on the test dataset, or whether further model improvement is possible. In biology, this problem is particularly pronounced as sample labels (response variables) are typically obtained through experiments and therefore have experiment noise associated with them. Such label noise puts a fundamental limit to the metrics of performance attainable by regression models on the test dataset.

Results

We address this challenge by deriving an expected upper bound for the coefficient of determination (R ²) for regression models when tested on the holdout dataset. This upper bound depends only on the noise associated with t

SUBMITTER: Li G 

PROVIDER: S-EPMC8243133 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature