Selecting a technique is just the beginning—evaluating its effectiveness is crucial to ensure the best results.
Key Evaluation Metrics
| Metric | Description | Best Used For |
|---|---|---|
| Accuracy | Measures the percentage of correctly classified samples. | Classification tasks |
| Precision & Recall | Precision measures how many predicted positives are correct; recall measures how many actual positives are identified. | Tasks with class imbalance (e.g., fraud detection, medical diagnosis) |
| F1‑Score | Harmonic mean of precision and recall. | General evaluation of classification performance |
| Mean Average Precision (mAP) | Measures precision across different IoU thresholds. | Object detection |
| IoU (Intersection over Union) | Measures the overlap between predicted and ground‑truth object bounding boxes. | Object detection, segmentation |
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