Recursive Random Forests Enable Better Predictive Performance and Model Interpretation than Variable Selection by LASSO-Reference-Cited by-同舟云学术

Recursive Random Forests Enable Better Predictive Performance and Model Interpretation than Variable Selection by LASSO

Published:2015-03-16 Issue:4 Volume:55 Page:736-746
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Zhu Xiang-Wei,Xin Yan-Jun,Ge Hui-Lin¹

Affiliation:

1. Hainan Provincial Key Laboratory of Quality and Safety for Tropical Fruits and Vegetables, Analysis and Testing Center, Chinese Academy of Tropical Agricultural Sciences, Haikou, 571101 Hainan, China

Funder

National Natural Science Foundation of China

Startup Foundation for Advanced Talents, Qingdao Agricultural University

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci500715e

Reference49 articles.

1. The Use of Substituent Constants in the Analysis of the Structure-Activity Relationship in Penicillin Derivatives

2. Variable Selection Methods in QSAR: An Overview

3. Molecular Descriptors in Chemoinformatics, Computational Combinatorial Chemistry, and Virtual Screening

4. The Problem of Overfitting

5. Is your QSAR/QSPR descriptor real or trash?

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