Variable Selection Using Random Forests
Plot of VSURF results
Real-world data on PM10 pollution in Rouen area, France
Predict method for VSURF object
Print of VSURF results
Summary of VSURF results
Tuning of the thresholding and interpretation steps of VSURF
Variable Selection Using Random Forests
Interpretation step of VSURF
Prediction step of VSURF
Thresholding step of VSURF
Three steps variable selection procedure based on random forests. Initially developed to handle high dimensional data (for which number of variables largely exceeds number of observations), the package is very versatile and can treat most dimensions of data, for regression and supervised classification problems. First step is dedicated to eliminate irrelevant variables from the dataset. Second step aims to select all variables related to the response for interpretation purpose. Third step refines the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purpose. Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015) <https://journal.r-project.org/archive/2015-2/genuer-poggi-tuleaumalot.pdf>.