An Interpretable Machine Learning Comparison for Employee Performance Prediction: Implications for Decision Support in Corporate Learning

Identifying employees who may need additional professional-learning support could help organisations allocate training resources, but model comparisons in this area often omit trivial baselines and class-specific metrics. We compare seven classifiers (decision tree, random forest, logistic regression, k-nearest neighbours, XGBoost, LightGBM, CatBoost) with a rule-based and a majority-class baseline under one protocol: stratified hold-out, the same tuning procedure, paired bootstrap confidence intervals, a descriptive repeated cross-validation check, and SHAP and LIME explanations. Experiment 1 predicts a KPI label of undocumented origin for 17,415 employees (35.9% positive); it is an illustrative comparison using the label as distributed, not validated evidence about KPI attainment. Experiment 2 predicts promotion for 54,808 employees (8.5% positive), a related task from the same public source and not an external validation. In Experiment 1, learning models reached macro-F1 of 0.628 to 0.663, against 0.590 for the rule-based baseline; a depth-5 decision tree was 0.005 below the reference random forest (95% bootstrap CI −0.016 to +0.005) but was not shown non-inferior at a 0.01 margin. In Experiment 2, boosting models were best (macro-F1 0.734 to 0.739), the decision tree was clearly worse (−0.030), and only about 35% of promoted employees were identified. Associations with training indicators are not causal, and no effect on decisions was tested.