Explore the significance of recall, a pivotal metric in classification models, calculating the accuracy of positive predictions among actual positives. Learn its importance and usage in model evaluations.
Table of Contents
Question
You created a classification model. Below is the confusion matrix for this model:
What is the name of the metric that uses TP/(TP+FN) expression for its value calculation?
Answer
Recall
Explanation
The confusion matrix (or error matrix) provides a tabulated view of predicted and actual values for each class. It is usually used as a performance assessment for classification models.
A binary confusion matrix is divided into four squares that represent the following values:
- True positive (TP) – the number of positive cases that the model predicted right.
- True negative (TN) – the number of negative cases that the model predicted right.
- False positive (FP) – the number of positive cases that the model falsely predicted right.
- False negative (FN) – the number of negative cases that the model falsely predicted right.
Recall metric defines how many positive cases that the model predicted are actually predicted right. We can calculate this metric using the following formula: TP/(TP+FN).
Accuracy is incorrect: a formula for Accuracy metric calculation is (TP+TN)/Total number of cases.
Precision is incorrect: a formula for Precision metric calculation is TP/(TP+FP).
F1 Score is incorrect: a formula for F1 metric calculation is 2TP/(2TP+FP+FN).
Selectivity is incorrect: this expression is for Selectivity (or true negative rate) metric calculation: TN/(TN+FP).
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.
