Answer Posted / Maimuna Khan

1. Precision: It is the ratio of correctly predicted positive instances to the total predicted positive instances. In other words, it measures how many of the predicted positives are actually true positives. Precision helps assess the model's ability to avoid false positives.
2. Recall (Sensitivity): It is the ratio of correctly predicted positive instances to the actual number of positive instances in the data. In other words, it measures how many of the actual positives are correctly identified by the model. Recall helps assess the model's ability to find all relevant instances.

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