How does the ROC Curve illustrate the performance of a binary classification model?
- Plots accuracy vs. error rate, shows overall performance
- Plots precision vs. recall, shows trade-off between sensitivity and specificity
- Plots true positive rate vs. false positive rate, shows trade-off between sensitivity and specificity
- nan
The ROC Curve plots the true positive rate against the false positive rate for different threshold values. This illustrates the trade-off between sensitivity (true positive rate) and specificity (true negative rate), helping to choose the threshold that best balances these two aspects.
Loading...
Related Quiz
- Why is ethics important in machine learning applications?
- How do features in Machine Learning differ from targets, and why are both necessary?
- The output of a GAN, after training, is a/an ________ that closely resembles the real data.
- In the Actor-Critic architecture, which part directly decides on the action to be taken?
- In the context of DBSCAN, if two core points are within the Epsilon distance of each other, they are said to be __________.