![]() ![]() Relation between Sensitivity, Specificity, FPR, and Threshold.ĥ. How to speculate the performance of the model?Ĥ. Defining terms used in AUC and ROC Curve.ģ. This blog aims to answer the following questions:Ģ. Note: For better understanding, I suggest you read my article about Confusion Matrix. It is also written as AUROC ( Area Under the Receiver Operating Characteristics) It is one of the most important evaluation metrics for checking any classification model’s performance. ![]() When we need to check or visualize the performance of the multi-class classification problem, we use the AUC ( Area Under The Curve) ROC ( Receiver Operating Characteristics) curve. So when it comes to a classification problem, we can count on an AUC - ROC Curve. In Machine Learning, performance measurement is an essential task. Understanding AUC - ROC Curve (Image courtesy: My Photoshopped Collection) ![]()
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