AI-900: How to Evaluate a Classification Model with True Positive Rate

Learn how to use the true positive rate metric to evaluate the performance of a classification model, and why other metrics such as MAE, RMSE, and R2 are not suitable for classification models.

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

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.

The correct answer is A. true positive rate. A classification model is a type of machine learning model that predicts a discrete label for a given input, such as spam or not spam for an email. To evaluate the performance of a classification model, we can use metrics that compare the predicted labels with the actual labels, such as accuracy, precision, recall, and F1-score. One of these metrics is the true positive rate, also known as sensitivity or recall, which measures the proportion of positive instances that are correctly classified by the model. The true positive rate is calculated as:

True Positive Rate=True Positives + False NegativesTrue Positives​

The other options are not suitable for evaluating a classification model. Mean absolute error (MAE) and root mean squared error (RMSE) are metrics that measure the average difference between the predicted and actual values for a regression model, which predicts a continuous value for a given input. Coefficient of determination (R2) is another metric for regression models that measures how well the model fits the data, by comparing the variation explained by the model with the total variation in the data.

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