One lucky accuracy score is not evidence. This churn classifier is evaluated the way I'd want to ...One lucky accuracy score is not evidence. This churn classifier is evaluated the way I'd want to ...
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One lucky accuracy score is not evidence. This churn classifier is evaluated the way I'd want to be audited: leakage-safe split (scaler fit on training rows only, 1,125 train / 375 held-out), a majority baseline it must beat, and five-fold cross-validation. Committed results: held-out ROC-AUC 0.7982 vs a 0.5000 majority baseline, 5-fold CV 0.819 +/- 0.0298 - beats the baseline, stable across folds. The model card also says what the model cannot tell you. The deliverable is not the classifier; it is an evaluation you can trust enough to make a decision with. Public code: github.com/jigonyoo/ml-churn-pipeline
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