Support vector regression that takes into consideration the importance of explanatory variables
Hiromasa Kaneko
Abstract
Abstract Support vector regression (SVR) is able to consider the nonlinear relationship between explanatory variables X and a target variable y to build a regression model with high predictive accuracy. Additionally, y values predicted with SVR models for new samples can exceed the actual y values in training data. However, because the Gaussian kernel, which is a kernel function generally used in SVR, is based on the Euclidean distance between samples, it is unable to consider the importance of X when building the regression model. Therefore, in this study, the focus was on the importance of X that can be calculated by random forests (RF), and a novel SVR method, called variable importance‐considering support vector regression (VI‐SVR), was proposed based on this importance. Because X is weighted based on importance, the greater the importance of X , the greater its contribution to the predicted value. Analysis using the spectral, quantitative structure–property relationship (QSPR), and quantitative structure–activity relationship (QSAR) datasets confirmed that the predictive accuracy of VI‐SVR was better than that of SVR. VI‐SVR Python code is available at https://github.com/hkaneko1985/dcekit .