Code reviewHardcr-g634

Subject Ml cross validationLevel Senior–Staff~20 minCommon in ML systems interviewsIndustries Software development

Question

Review this Python cross-validation for a medical-imaging classifier.

CV AUC is 0.95 but a prospective study gets 0.70. Explain the gap.

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Code to reviewpython

1import numpy as np2from sklearn.model_selection import cross_val_score3from sklearn.svm import SVC4 5def evaluate(X, y):6    # each patient contributes ~8 image patches; X is one row per patch7    clf = SVC(kernel="rbf", probability=False)8    scores = cross_val_score(clf, X, y, cv=5, scoring="roc_auc")9    print("CV AUC:", scores.mean())10    return scores.mean()

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