Employee Turnover Prediction With Deep Learning - DZone AI

#artificialintelligence

According to a study from Catalyst, the cost of replacing an employee is around 50% to 75% of the employee's annual salary, on average. Considering a mid-level position with a monthly salary of 20,000 pesos, the total cost of replacing this employee would be around 140,000 pesos. On average, it takes around 50 days to replace an employee, and the costs incurred due to productivity loss will keep adding up. For a big company like everis with over 20,000 employees, considering a turnover rate of 15% and an average salary of $15,000 pesos, the total annual cost of turnover would rise up to at least 270 million pesos.


HR Analytics: Using Machine Learning to Predict Employee Turnover

#artificialintelligence

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. 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.


HR Analytics: Using Machine Learning to Predict Employee Turnover

#artificialintelligence

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. 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. We can't stress how excited we are to share this post because it's a much needed step towards machine learning in business applications!!! Enjoy.


Akron Zips debut turnover pencil, things immediately get dangerous

FOX News

Fox News Flash top headlines for Sept. 9 are here. Check out what's clicking on Foxnews.com The Akron Zips might be better off turning over their "turnover pencil" so they can use its eraser to get rid of the idea altogether. The Zips may have lost to the UAB Blazers to fall to 0-2 on the season Saturday, but the defense's newest prop had plenty of eyes on it -- unfortunately one of those eyes came a bit too close. Akron became the latest team to unleash a new twist on the Miami Hurricanes' turnover chain.


Analyzing Employee Turnover - Predictive Methods

#artificialintelligence

At first glance, 'intent to leave' seems like it should be pretty good predictor of turnover. If a coworker told me that they were going to quit, I feel like I'd have a pretty good sense of how likely they were to leave. However, many researchers have developed constructs to measure this intention and the results are surprising. For example, there was a meta-analytic study (i.e., study of studies) in 2000 by Rodger Griffeth and Peter Hom on turnover that found the construct'intent to leave' had a shared variance with actually leaving of 12% across all studies (explains roughly 12% of why people leave). That's pretty good for a study on human behavior, but it does leave a reader wondering what is going on.