What does a high precision indicate in a classification model?
Practice Questions
Q1
What does a high precision indicate in a classification model?
A high number of true positives compared to false positives
A high number of true positives compared to false negatives
A high overall accuracy
A high number of true negatives
Questions & Step-by-Step Solutions
What does a high precision indicate in a classification model?
Step 1: Understand what precision means. Precision is a measure of how many of the predicted positive instances are actually positive.
Step 2: Identify the components of precision. It is calculated using the formula: Precision = True Positives / (True Positives + False Positives).
Step 3: Recognize what true positives are. True positives are the cases where the model correctly predicts a positive instance.
Step 4: Understand what false positives are. False positives are the cases where the model incorrectly predicts a positive instance when it is actually negative.
Step 5: Realize that high precision means there are many true positives and few false positives. This indicates that the model is effective at correctly identifying positive instances.
Step 6: Conclude that a high precision is desirable in a classification model, especially when the cost of false positives is high.
