How will you define precision in a confusion matrix?
A. It is the ratio of true positive to false negative predictions.
B. It is the measure of how accurately a model can identify positive classes out of all the positive classes present in the dataset.
C. It is the measure of how accurately a model can identify true positives from all the positive predictions that it has made
D. It is the measure of how accurately a model can identify true negatives from all the positive predictions that it has made
Answer:C
How will you define precision in a confusion matrix?
A. It is the ratio of true positive to false negative predictions. B. It is the measure of how accurately a model can identify positive classes out of all the positive classes present in the dataset. C. It is the measure of how accurately a model can identify true positives from all the positive predictions that it has made D. It is the measure of how accurately a model can identify true negatives from all the positive predictions that it has made
Explanation: Precision is the ratio of true positive and (true positive + false positive), which means that it measures, out of all the positive predicted values by a model, how precisely a model predicted the truly positive values.