Define Precision and Recall.

Precision and Recall are two fundamental concepts in the field of Information Retrieval and Machine Learning, often used when evaluating models.

1. Precision: Precision is a measure that tells us what proportion of predicted Positive observations is truly Positive. It is a measure of a classifier's exactness. Low precision indicates a high number of False Positives.

Formula for Precision: Precision = True Positives / (True Positives + False Positives)

2. Recall: Recall, also known as Sensitivity, Hit Rate, or True Positive Rate, is a measure that tells us what proportion of actual Positive observations was identified correctly. It is a measure of a classifier's completeness. Low recall indicates a high number of False Negatives.

Formula for Recall: Recall = True Positives / (True Positives + False Negatives)

In simple terms, Precision is about being precise. So, even if we managed to capture only a few actual positive observations, as long as we captured them correctly, our precision score can be high. On the other hand, Recall is not about being precise but about capturing the maximum number of actual positive observations. So, even if we captured a lot of negative observations as positive, as long as we captured most of the actual positive observations, our recall score can be high.