Learn how the true positive rate metric assesses the accuracy of classification models in identifying actual positives, crucial in evaluating model performance.
Table of Contents
Question
Which metric can you use to evaluate a classification model?
A. true positive rate
B. mean absolute error (MAE)
C. coefficient of determination (R2)
D. root mean squared error (RMSE)
Answer
A. true positive rate
Explanation
The true positive rate, also known as sensitivity or recall, measures the proportion of actual positives that were correctly identified by the model. It’s an important metric in evaluating how well a classification model identifies positive instances from the entire actual positive class.
What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.
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