F1-Score

INFO

Harmonic mean of precision and recall, providing a balanced measure.

How It Works

$$\text{F1}=2\cdot \frac{\text{Precision}\cdot \text{Recall}}{\text{Precision}+\text{Recall}}$$

• Combines Precision and Recall into a single score
• Sensitive to both false positives and false negatives

What to Look For

• Ideal for imbalanced datasets
• Useful when both FP and FN matter
• A single score to summarize model performance

Application Models

• Convolutional Neural Network (CNN)
• Deep Neural Network (DNN)
• Transformer