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Precision-Recall Curve

It is a graph that shows the trade-off between precision and recall of a model according to a decision threshold. Precision refers to the proportion of correctly identified positive instances, while recall refers to the proportion of positive instances in the dataset that the model correctly identified. The precision-recall curve is useful for evaluating the performance of a classifier in problems with imbalanced classes.


Fraud Detection:

  • Evaluate and optimize models to identify fraudulent transactions with high precision and recall.

Medical Diagnosis:

  • Evaluate classification models for diseases where it is crucial to minimize false negatives (not detecting a disease) and false positives (misdiagnosing).

Spam Filtering:

  • Adjust models to detect unwanted emails (spam) while minimizing legitimate emails incorrectly marked as spam.

Recommendation Systems:

  • Evaluate the effectiveness of recommendation algorithms to identify relevant items for users with high precision and recall.

Object Detection in Images:

  • Optimize computer vision models to detect specific objects in images with high precision and recall.

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