🟪 1-Minute Summary
Accuracy is the proportion of correct predictions: (TP + TN) / Total. Simple and intuitive, but MISLEADING for imbalanced datasets. If 95% of emails are not spam, a model that always predicts “not spam” gets 95% accuracy but is useless. Use accuracy only for balanced datasets; prefer precision, recall, or F1 for imbalanced data.
🟦 Core Notes (Must-Know)
Formula
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When to Use Accuracy
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When NOT to Use Accuracy
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The Imbalanced Data Problem
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“Your model has 95% accuracy. Is it good?”
- [Answer: Need to know class distribution first!]
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“Why is accuracy misleading for imbalanced datasets?”
- [Answer: Majority class dominates the metric]
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“What metric would you use instead of accuracy for fraud detection?”
- [Answer: Recall or F1 - fraud is rare but important to catch]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using accuracy as the only metric
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Mistake 2: Not checking class distribution first
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🟩 Mini Example (Quick Application)
Scenario
[Imbalanced dataset accuracy trap]
Solution
from sklearn.metrics import accuracy_score
# Example showing why accuracy can be misleading
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