Home care agencies might want to think twice about how they handle workers who are chronically late to client visits. "When we started Honor, we thought, clearly, if a care pro is late, that's terrible," Seth Sternberg, CEO of the San Francisco-based company, told Home Health Care News. "And then we learned that's not true." That counterintuitive lesson about late workers came from analyzing data gathered through Honor's proprietary technology platform. About three years after launching, Honor now is looking closely at its data and adjusting operations accordingly, in a variety of ways.
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper.
As advanced machine learning algorithms are gaining acceptance across many organizations and domains, machine learning interpretability is growing in importance to help extract insight and clarity regarding how these algorithms are performing and why one prediction is made over another. There are many methodologies to interpret machine learning results (i.e. However, some recent R packages that focus purely on ML interpretability agnostic to any specific ML algorithm are gaining popularity. One such package is DALEX and this post covers what this package does (and does not do) so that you can determine if it should become part of your preferred machine learning toolbox. We implement machine learning models using H2O, a high performance ML toolkit. Let's see how DALEX and H2O work together to get the best of both worlds with high performance and feature explainability!
As new technologies are discovered and developed, widespread adoption doesn't occur until viable business benefits can be identified and validated. Machine Learning, Cognitive Computing or Artificial Intelligence (depending on what you call it) is a "hot," interesting new technology development, and one that is quickly proceeding through the hype cycle to widespread adoption. As a practical use case, Machine Learning can now be used to gain new, perhaps even unexpected insights into sales team engagement to predict sales turnover.
Today's guest blogger, Toshi Takeuchi used machine learning on a job-related dataset for predictive analytics. Let's see what he learned. Companies spend money and time recruiting talent and they lose all that investment when people leave. Therefore companies can save money if they can intervene before their employees leave. Perhaps this is a sign of a robust economy, that one of the datasets popular on Kaggle deals with this issue: Human Resources Analytics - Why are our best and most experienced employees leaving prematurely?