What is Model Explainability and how can it give you more insight into Machine Learning predictions?
Survival analysis for employee churn – Part 2: Censoring
In his first video on Survival Analysis, Thomas introduced the concept of a survival function and how we can use this to compare different demographics in the case of churn.
However, an important point that was omitted here was the notion of censoring – the ability to take into account missing data, whereby the time to event is not observed. In this video, he takes a deeper dive into censoring.
He discusses the different types of censoring, looks into an example of a medical study, and shows you how this can be applied to HR.
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Introducing the concept of Survival Analysis and how it can help you understand churn.