Speculating the performance of the model
- A high AUC (close to 1) indicates excellent discriminative power. This means the model is effective in distinguishing between the two classes, and its predictions are reliable.
- A low AUC (close to 0) suggests poor performance. In this case, the model struggles to differentiate between the positive and negative classes, and its predictions may not be trustworthy.
- AUC around 0.5 implies that the model is essentially making random guesses. It shows no ability to separate the classes, indicating that the model is not learning any meaningful patterns from the data.
AUC ROC Curve in Machine Learning
One important aspect of Machine Learning is model evaluation. You need to have some mechanism to evaluate your model. This is where these performance metrics come into the picture they give us a sense of how good a model is. If you are familiar with some of the basics of Machine Learning then you must have come across some of these metrics, like accuracy, precision, recall, auc-roc, etc., which are generally used for classification tasks. In this article, we will explore in depth one such metric, which is the AUC-ROC curve.
Table of Content
- What is the AUC-ROC curve?
- Key terms used in AUC and ROC Curve
- Relationship between Sensitivity, Specificity, FPR, and Threshold.
- How does AUC-ROC work?
- When should we use the AUC-ROC evaluation metric?
- Speculating the performance of the model
- Understanding the AUC-ROC Curve
- Implementation using two different models
- How to use ROC-AUC for a multi-class model?
- FAQs for AUC ROC Curve in Machine Learning