🟪 1-Minute Summary
Classification report provides a comprehensive view of model performance: precision, recall, F1-score, and support for each class. Shows how well the model performs overall and per-class. Essential for imbalanced datasets where accuracy alone is misleading. Use to identify which classes the model struggles with.
🟦 Core Notes (Must-Know)
What’s in a Classification Report?
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Metrics Explained
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- Precision
- Recall
- F1-Score
- Support
Macro vs Weighted Averages
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When to Use
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“How do you interpret a classification report?”
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“What’s the difference between macro and weighted average?”
- [Answer: Macro = unweighted average, Weighted = weighted by support]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Only looking at overall accuracy
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🟩 Mini Example (Quick Application)
Scenario
[Multi-class classification evaluation]
Solution
from sklearn.metrics import classification_report
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🔗 Related Topics
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