What does ROC AUC measure in a classification model?
Practice Questions
1 question
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
ROC AUC measures the area under the ROC curve, indicating the model's ability to distinguish between classes.
Questions & Step-by-step Solutions
1 item
Q
Q: What does ROC AUC measure in a classification model?
Solution: ROC AUC measures the area under the ROC curve, indicating the model's ability to distinguish between classes.
Steps: 5
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.
