A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling.

ABSTRACT: Random forest classification is a popular machine learning method for developing prediction models in many research settings. Often in prediction modeling, a goal is to reduce the number of variables needed to obtain a prediction in order to reduce the burden of data collection and improve efficiency. Several variable selection methods exist for the setting of random forest classification; however, there is a paucity of literature to guide users as to which method may be preferable for different types of datasets. Using 311 classification datasets freely available online, we evaluate the prediction error rates, number of variables, computation times and area under the receiver operating curve for many random forest variable selection methods. We compare random forest variable selection met

SUBMITTER: Speiser JL 

PROVIDER: S-EPMC7508310 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature