What does the ROC curve represent in model evaluation?
Practice Questions
Q1
What does the ROC curve represent in model evaluation?
Relationship between precision and recall
Trade-off between true positive rate and false positive rate
Model training time vs accuracy
Data distribution visualization
Questions & Step-by-Step Solutions
What does the ROC curve represent in model evaluation?
Step 1: Understand that the ROC curve is a graphical representation used to evaluate the performance of a classification model.
Step 2: Know that the ROC curve plots two important rates: the true positive rate (TPR) and the false positive rate (FPR).
Step 3: The true positive rate (sensitivity) measures how many actual positive cases were correctly identified by the model.
Step 4: The false positive rate measures how many actual negative cases were incorrectly identified as positive by the model.
Step 5: The ROC curve shows how the TPR and FPR change when you adjust the threshold for classifying a positive case.
Step 6: A model with a better performance will have a ROC curve that is closer to the top-left corner of the graph, indicating a high TPR and a low FPR.
