ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2020, Vol. 28 ›› Issue (10): 1777-1788.doi: 10.3724/SP.J.1042.2020.01777

• Research Method • Previous Articles    

Lasso regression: From explanation to prediction

ZHANG Lijin1, WEI Xiayan2, LU Jiaqi2, PAN Junhao1   

  1. 1Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China;
    2Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China
  • Received:2019-12-13 Online:2020-10-15 Published:2020-08-24

Abstract: Regression analysis, a method to evaluate the relationship between variables, is widely used in psychological studies. However, due to its highly focus on the interpretation of sample data, the traditional ordinary least squares regression has several drawbacks, such as over-fitting problem and limitation on dealing with multicollinearity, which may undermine the generalizability of the model. With the rapid development of methodology research, a shift from focusing on interpretation of the regression coefficients to improving the prediction of the model has emerged and become more and more important. Least absolute shrinkage and selection operator (Lasso) regression has been emerged to better compensate for the limitations of traditional methods. By introducing a penalty term in the model and shrinking the regression coefficients to zero, Lasso regression can achieve a higher accuracy of model prediction and model generalizability with the cost of a certain estimation bias. Besides, Lasso regression can also effectively deal with the multicollinearity problem. Therefore, it is helpful for the construction and improvement of psychological theory.

Key words: regression, regularization, Lasso, prediction

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