When to Use Relative Weights Over Shapley
Shapley regression is a popular method for estimating the importance of predictor variables in linear regression. This method can deal with highly correlated predictor variables that are frequently encountered in real-world data. Shapley regression has been gaining popularity in recent years and has been (re-)invented multiple times. In this blog post, I explain why a newer method, relative […]
Shapley Regression Vs. Relative Weights | Understanding The Differences
Shapley regression and Relative Weights are two methods for estimating the importance of predictor variables in linear regression. Studies have shown that the two, despite being constructed in very different ways, provide surprisingly similar scores((Grömping, U. (2015). Variable importance in regression models, WIREs Comput Stat 7, 137-152.))((Lebreton, J.M., Ployhart, R.E., & Ladd, R.T. (2004). A Monte Carlo Comparison of Relative […]
You Can Now Run Shapley Regression in Displayr
Shapley Regression, also known as Shapley Value Regression, is the leading method for driver analysis. It calculates the importance of different predictors in explaining an outcome variable and is prized for its ability to address multicollinearity. You can now use Shapley Regression in Displayr. How to compute Shapley Regression in Displayr Go to Insert > […]
What is Shapley Value Regression?
Shapley Value regression is a technique for working out the relative importance of predictor variables in linear regression. Its principal application is to resolve a weakness of linear regression, which is that it is not reliable when predicted variables are moderately to highly correlated. Shapley Value regression is also known as Shapley regression, Shapley Value […]
