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Analyze Model Performance
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In LandingLens, have you ever wondered what the score in the Confidence Threshold is for? Or have you thought about what knowledge you can gain from analyzing a model's precision and recall? This video covers it all—from understanding the confidence score to True Positives, True Negatives, False Positives, and False Negatives.
Notes:
- Confidence thresholds are only applicable to Object Detection and Segmentation projects.
- For Object Detection, the F1 score combines precision and recall into a single score, creating a unified measure that assesses the model’s effectiveness in minimizing false positives and false negatives. For Classification, the F1, Precision, and Recall scores are identical. This is because Classification models have only two prediction outcomes: "Correct" and "Misclassified".
Notes:
- Confidence thresholds are only applicable to Object Detection and Segmentation projects.
- For Object Detection, the F1 score combines precision and recall into a single score, creating a unified measure that assesses the model’s effectiveness in minimizing false positives and false negatives. For Classification, the F1, Precision, and Recall scores are identical. This is because Classification models have only two prediction outcomes: "Correct" and "Misclassified".