What does precision indicate in a confusion matrix?
Practice Questions
1 question
Q1
What does precision indicate in a confusion matrix?
The ratio of true positives to the total predicted positives
The ratio of true positives to the total actual positives
The overall correctness of the model
The ability to identify all relevant instances
Precision measures the accuracy of positive predictions, calculated as the number of true positives divided by the sum of true positives and false positives.
Questions & Step-by-step Solutions
1 item
Q
Q: What does precision indicate in a confusion matrix?
Solution: Precision measures the accuracy of positive predictions, calculated as the number of true positives divided by the sum of true positives and false positives.
Steps: 6
Step 1: Understand that a confusion matrix is a table used to evaluate the performance of a classification model.
Step 2: Identify the components of the confusion matrix: True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN).
Step 3: Focus on the positive predictions, which are represented by True Positives and False Positives.
Step 4: Recognize that Precision is a measure of how many of the predicted positive cases were actually positive.
Step 5: Calculate Precision using the formula: Precision = TP / (TP + FP).
Step 6: Interpret the result: A higher precision means that more of the positive predictions are correct.
