LIVE DataTalk on HR Analytics Tonight: Using Machine Learning to Predict Employee Turnover

Written by Matt Dancho on October 26, 2017





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Tonight at 7PM EST, we will be giving a LIVE #DataTalk on Using Machine Learning to Predict Employee Turnover. Employee turnover (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. We used two cutting edge techniques: 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 used the new lime package that enables breakdown of complex, black-box machine learning models into variable importance plots. The talk will cover HR Analytics and how we used R, H2O, and LIME to predict employee turnover.

DataTalk Details

The DataTalk is FREE and LIVE. You can sign up for a reminder on Experian DataLab’s website here: Machine Learning for Recruitment & Reducing Employee Attrition.

  • Analysis: We used H2O and LIME to predict employee turnover. Here’s the LIME feature importance visualization.

HR LIME Feature Importance Visualization

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More details can be found on Experian DataLabs Website here. DataTalk

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