A model can have high accuracy and still be unsafe.
Example: A fraud model with 99% accuracy may miss rare fraud cases. If fraud is only 1% of data, predicting “not fraud” every time also gives 99% accuracy—but the business loses money.
Before choosing a model, define the cost of each mistake:
Model task:
[Example: Predict loan default]
Positive prediction:
[Example: Reject loan]
Negative prediction:
[Example: Approve loan]
False positive cost:
[Example: Good customer rejected]
False negative cost:
[Example: Risky customer approved]
Rare-event cost:
[Example: High-loss default]
Required decision threshold:
[Example: Approve only if default probability < 10%]
Evaluation requirements:
1. Confusion matrix
2. Precision, recall, F1
3. Calibration check
4. Performance by important groups
5. Business impact estimate
💡 Why it matters:
A confusion matrix shows true positives, false positives, false negatives, and true negatives. Precision and recall mean different things depending on which mistake is more expensive.
🎯 Beginner rule:
Do not optimize accuracy first. Optimize the metric that matches the real-world cost of being wrong.
📌 Example:
For medical screening, a false negative may be more dangerous than a false positive. For spam filtering, a false positive may be more annoying than a false negative.
🔗 Research next:
Scikit-learn Model Evaluation
Scikit-learn Probability Calibration
Google People + AI Guidebook
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