Parameter importance analysis: Random forest approach

Abstract

Abstract During surface roughness modelling, it is crucial to determine the parameters with the highest predictive power since these are the outcome drivers. Based on out-of-bag (OOB) mean square error, the following Random Forest techniques have been utilized to determine parameter importance: mean decrease in accuracy and total increase in node purity. Validation of the results has been achieved using the Bayesian linear regression technique. The PMMA machining experiment has been designed by the Central Composite Design (CCD) Face Centered technique. Cutting speed, feed rate and depth of cut are the control parameters, while surface quality is the dependent parameter. The authors established that the random forest regression algorithm yields an OOB mean squared error of 0.113 and that the OOB mean squared error decreases with increasing number of trees for validation dataset. On the other hand, the OOB mean squared error increases with increasing number of trees for training dataset. Both the mean decrease in accuracy and total increase in node purity techniques reveal that the order of decreasing machining parameter importance is as follows: cutting speed, depth of cut and feed rate. Validation of the obtained results yields the same outcome. Hence, feed rate may be omitted from models for faster and simpler surface roughness prediction.