What does ROC AUC measure in a classification model?
Practice Questions
Q1
What does ROC AUC measure in a classification model?
The area under the Receiver Operating Characteristic curve
The average precision of the model
The total number of true positives
The mean error of predictions
Questions & Step-by-Step Solutions
What does ROC AUC measure in a classification model?
Step 1: Understand that ROC stands for Receiver Operating Characteristic, which is a graphical representation of a model's performance.
Step 2: Know that the ROC curve plots the True Positive Rate (sensitivity) against the False Positive Rate (1 - specificity) at various threshold settings.
Step 3: Realize that AUC stands for Area Under the Curve, which quantifies the overall ability of the model to distinguish between the positive and negative classes.
Step 4: A higher AUC value (close to 1) means the model is better at distinguishing between classes, while a value around 0.5 indicates no discrimination (similar to random guessing).
Step 5: Conclude that ROC AUC is a useful metric to evaluate the performance of a classification model.
