Micro F1 Score
Micro F1 Score
The Micro F1 Score is an evaluation metric used primarily in multi-class and multi-label classification tasks. Unlike Macro F1 Score, it calculates global counts of true positives, false positives, and false negatives across all classes, then uses these to compute a single Precision, Recall, and F1 Score.
It is most useful when the dataset is imbalanced and you care more about overall performance than per-class fairness.
Definition
Where:
Instead of averaging individual class scores, Micro F1 aggregates global totals of:
- True Positives (TP)
- False Positives (FP)
- False Negatives (FN)
Step-by-Step Example
Suppose a 3-class classification problem with:
- Class A: TP=50, FP=10, FN=5
- Class B: TP=30, FP=15, FN=10
- Class C: TP=20, FP=5, FN=15
Global totals:
- TP = 50 + 30 + 20 = 100
- FP = 10 + 15 + 5 = 30
- FN = 5 + 10 + 15 = 30
Micro Precision and Recall are equal, so Micro F1 equals them.
Micro vs Macro vs Weighted F1
Metric | How It Works | Best For |
---|---|---|
Micro F1 | Global average across all classes (TP, FP, FN summed first) | Imbalanced data where you care about overall performance |
Macro F1 | Average of F1 scores per class (unweighted) | Equal importance for each class |
Weighted F1 | Average of F1 scores per class (weighted by class size) | Imbalanced data, focus on majority classes |
Use Cases
- Multi-label text classification
- Image tagging tasks
- Medical diagnosis systems with multiple labels
- Imbalanced datasets with focus on global accuracy
Limitations
- May **hide poor performance** on minority classes
- Doesn't reflect per-class fairness
Related Pages
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