Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications.

ABSTRACT: Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR). With the growing number of ensemble learning models such as random forest, the effectiveness of QSAR/QSPR will be limited by the machine's inability to interpret the predictions to researchers. In fact, many implementations of ensemble learning models are able to quantify the overall magnitude of each feature. For example, feature importance allows us to assess the relative importance of features and to interpret the predictions. However, different ensemble learni

SUBMITTER: Chen CH 

PROVIDER: S-EPMC7106596 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature