Evaluating High-Variance Leaves as Uncertainty Measure for Random Forest Regression.

ABSTRACT: Uncertainty measures estimate the reliability of a predictive model. Especially in the field of molecular property prediction as part of drug design, model reliability is crucial. Besides other techniques, Random Forests have a long tradition in machine learning related to chemoinformatics and are widely used. Random Forests consist of an ensemble of individual regression models, namely, decision trees and, therefore, provide an uncertainty measure already by construction. Regarding the disagreement of single-model predictions, a narrower distribution of predictions is interpreted as a higher reliability. The standard deviation of the decision tree ensemble predictions is the default uncertainty measure for Random Forests. Due to the increasing application of machine learning in drug desig

SUBMITTER: Dutschmann TM 

PROVIDER: S-EPMC8588039 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature