Learn what AUC score means and how to use it to evaluate binary classification models. Find out how AUC score relates to ROC curve, TPR, FPR, accuracy, and variance.
Table of Contents
Question
You use an Azure Machine Learning designer pipeline to train and test a binary classification model. You review the model’s performance metrics in an Evaluate Model module, and note that it has an AUC score of 0.6. What can you conclude about the model?
A. The model performs better than random guessing
B. The model predicts accurately for 40% of cases
C. The model can explain 60% of the variance between true and predicted labels.
Answer
A. The model performs better than random guessing
Explanation
The higher the score of AUC, the better the performance of the model.
The correct answer is A. The model performs better than random guessing.
The AUC score is a measure of how well a binary classification model can distinguish between the positive and negative classes. It is calculated by plotting the True Positive Rate (TPR) against the False Positive Rate (FPR) for all possible threshold values, and finding the area under the ROC curve. The ROC curve is a graph that shows the trade-off between sensitivity (TPR) and specificity (1-FPR) for a binary classifier.
The AUC score ranges from 0 to 1, where 0 means the model is completely wrong and 1 means the model is completely right. A random guessing model would have an AUC score of 0.5, meaning it has no ability to discriminate between the classes. Therefore, a model with an AUC score of 0.6 performs better than random guessing, but not by much. It means that the model has a 60% chance of ranking a random positive example higher than a random negative example.
The AUC score does not tell us anything about the accuracy of the model, which is the percentage of correct predictions out of all predictions. The accuracy depends on the chosen threshold value, which determines how the model classifies the examples based on the predicted probabilities or scores. The AUC score also does not tell us anything about the variance or the error of the model, which are measures of how much the model deviates from the true labels or the expected value.
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