What is recall score in machine learning?

What is recall score in machine learning?

Recall score, also known as sensitivity, measures the ability of a model to identify all relevant instances. It's calculated as the ratio of true positives to the sum of true positives and false negatives.

How do I calculate recall score in Python?

You can calculate recall score in Python using the recall_score function from the sklearn.metrics module. Here's a simple example:

from sklearn.metrics import recall_score

y_true = [1, 0, 1, 1, 0, 1]
y_pred = [1, 0, 1, 0, 0, 1]

recall = recall_score(y_true, y_pred)
print('Recall Score:', recall)

What is a good recall score?

A good recall score depends on the context of the problem. Generally, a recall score closer to 1 indicates better performance, especially in cases where missing a positive instance is critical.

Can you explain the difference between recall and precision?

Sure! Recall measures the ability to find all relevant instances, while precision measures the accuracy of the positive predictions. High recall with low precision means many false positives, and vice versa.