Standard 7 stops to get here

Average Precision

The weighted mean of precisions at each threshold, where the weight is the increase in recall from the previous threshold.

Your route here

7 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Supervised Learning ✓ understood

    Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.

  3. Classification ✓ understood

    A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.

  4. Confusion Matrix ✓ understood

    A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.

  5. Precision ✓ understood

    The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive.

  6. Recall ✓ understood

    The proportion of true positives among all actual positives - measures how many actual positives were correctly identified.

  7. Precision-Recall Curve ✓ understood

    A curve showing the tradeoff between precision and recall at different thresholds.

  8. Average Precision · you are here ✓ understood

The weighted mean of precisions at each threshold, where the weight is the increase in recall from the previous threshold.

This concept is essential for understanding model evaluation & metrics and forms a key part of modern AI systems.

  • Precision-Recall Curve
  • mAP
  • Evaluation

Where it sits

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Precision-Recall Curve
Average Precision

Leads to

Nothing yet: a destination in its own right.

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Evaluation Intersection over Union A metric for object detection measuring overlap between predicted and ground truth bounding boxes. Vision & Multimodal Object Detection Finding every object of interest in an image and giving each a class label, a confidence score and a bounding box. Evaluation AUC Area Under the Curve - measures the area under the ROC curve, indicating classification model performance (1.0 is perfect). Evaluation Precision The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive.