Employee turnvover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. In this post, we’ll use two cutting edge techniques. First, we’ll use the
h2o package’s new FREE automatic machine learning algorithm,
h2o.automl(), to develop a predictive model that is in the same ballpark as commercial products in terms of ML accuracy. Then we’ll use the new
lime package that enables breakdown of complex, black-box machine learning models into variable importance plots.
Source: R-bloggers (R news and tutorials contributed by (750) R bloggers)