Evaluating the model

HR Analytics: Predicting Employee Churn in Python

Hrant Davtyan

Assistant Professor of Data Science American University of Armenia

Prediction errors

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HR Analytics: Predicting Employee Churn in Python

Evaluation metrics (1)

  • If target is leavers, focus on FN
    • Recall score = TP/(TP+FN)
    • Lower FN, higher Recall score
    • Recall score - % of correct predictions among 1s (leavers)
  • If target is stayers, focus on FP
    • Specificity = TN/(TN+FP)
    • Lower FP, higher Specificity,
    • Specificity - % of correct predictions among 0s (stayers)
HR Analytics: Predicting Employee Churn in Python

Evaluation metrics (2)

  • Even if target is leavers, you may still focus on FP:
    • Precision score = TP/(TP+FP)
    • Lower FP, higher Precision score
    • Precision score - % of leavers in reality, among those predicted to leave
HR Analytics: Predicting Employee Churn in Python

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HR Analytics: Predicting Employee Churn in Python

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