Application of random forests method to predict the retention indices of some polycyclic aromatic hydrocarbons

In this work, a quantitative structure–retention relationship (QSRR) investigation was carried out based on the new method of random forests (RF) for prediction of the retention indices (RIs) of some polycyclic aromatic hydrocarbon (PAH) compounds. The RIs of these compounds were calculated using the theoretical descriptors generated from their molecular structures. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. Optimization of these parameters showed that in the point m = 70, nt = 460, the RF method can give the best results. Also, performance of the RF model was compared with that of the artificial neural network (ANN) and multiple linear regression (MLR) techniques. The results obtained show the relative superiority of the RF method over the MLR and ANN ones.