Code reviewMediumcr-g623

Subject Ml evaluationLevel Mid–Senior~16 minCommon in ML systems interviewsIndustries Software development

Question

Review this Python fraud-detection evaluation.

The model prints 99.51% accuracy. Should it ship?

What a strong answer looks like

Separate real bugs from style. Rank issues by severity, point at the root cause rather than the symptom, and suggest a concrete fix — specific and kind.

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Code to reviewpython

1import numpy as np2from sklearn.ensemble import RandomForestClassifier3from sklearn.metrics import accuracy_score4 5# y: 0 = legit, 1 = fraud; about 0.5% of rows are fraud6def evaluate(X_tr, y_tr, X_te, y_te):7    clf = RandomForestClassifier(n_estimators=200, random_state=0)8    clf.fit(X_tr, y_tr)9    preds = clf.predict(X_te)10    acc = accuracy_score(y_te, preds)11    print(f"accuracy: {acc:.4f}")  # prints 0.995112    if acc > 0.99:13        print("shipping it")14    return acc

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