Recursive random forests enable better predictive performance and model interpretation than variable selection by LASSO

XW Zhu, YJ Xin, HL Ge - Journal of chemical information and …, 2015 - ACS Publications
Variable selection is of crucial significance in QSAR modeling since it increases the model
predictive ability and reduces noise. The selection of the right variables is far more …
被引用次数：39 相关文章
[PDF] researchgate.net

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

XW Zhu, YJ Xin, HL Ge - J. Chem. Inf. Model, 2015 - researchgate.net
Variable selection is of crucial significance in QSAR modeling since it increases the model
predictive ability and reduces noise. The selection of the right variables is far more …

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

XW Zhu, YJ Xin, HL Ge - Journal of Chemical Information and …, 2015 - europepmc.org
Variable selection is of crucial significance in QSAR modeling since it increases the model
predictive ability and reduces noise. The selection of the right variables is far more …

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

XW Zhu, YJ Xin, HL Ge - Journal of chemical information …, 2015 - pubmed.ncbi.nlm.nih.gov
Variable selection is of crucial significance in QSAR modeling since it increases the model
predictive ability and reduces noise. The selection of the right variables is far more …