In the context of a confusion matrix, what does precision measure?

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Question: In the context of a confusion matrix, what does precision measure?

Options:

Correct Answer: Correct positive predictions out of total positive predictions

Solution:

Precision measures the ratio of true positive predictions to the total predicted positives, indicating the accuracy of positive predictions.

In the context of a confusion matrix, what does precision measure?

In the context of a confusion matrix, what does precision measure?

Step 1: Understand what a confusion matrix is. It is a table that helps us see how well a model is performing by showing true positives, false positives, true negatives, and false negatives.
Step 2: Identify what true positives are. True positives are the cases where the model correctly predicts a positive outcome.
Step 3: Identify what predicted positives are. Predicted positives are the total number of cases the model predicted as positive, which includes both true positives and false positives.
Step 4: Learn the formula for precision. Precision is calculated as: Precision = True Positives / (True Positives + False Positives).
Step 5: Understand what precision tells us. Precision measures how many of the predicted positive cases were actually positive, indicating the accuracy of the positive predictions.

Precision – Precision is the ratio of true positive predictions to the total predicted positives, reflecting the accuracy of the positive predictions made by a model.