The Effects of Variable Selection Methods on Linear Regression-Based Effort Estimation Models

Stepwise regression has often been used for variable selection of effort estimation models. However it has been criticized for inappropriate selection, and another method is recommended. We thus examined the effects of Lasso, which is one of such variable selection methods. An experiment with datasets from PROMISE repository revealed that Lasso-based selection stably selected better variables than stepwise in predictive performance. We thus concluded Lasso-based selection is preferable to stepwise regression.