Friday, 29 January 2021

AUC ROC Concept

 In Machine Learning, performance measurement is an essential task. So when it comes to a classification problem, we can count on an AUC - ROC Curve. 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. It is one of the most important evaluation metrics for checking any classification model’s performance. It is also written as AUROC (Area Under the Receiver Operating Characteristics)

What is the AUC - ROC Curve?

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AUC - ROC Curve [Image 2] (Image courtesy: My Photoshopped Collection)

Defining terms used in AUC and ROC Curve.

TPR (True Positive Rate) / Recall /Sensitivity

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Specificity

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FPR

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How to speculate about the performance of the model?

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[Image 6 and 7] (Image courtesy: My Photoshopped Collection)
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[Image 8 and 9] (Image courtesy: My Photoshopped Collection)
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[Image 10 and 11] (Image courtesy: My Photoshopped Collection)
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[Image 12 and 13] (Image courtesy: My Photoshopped Collection)

The relation between Sensitivity, Specificity, FPR, and Threshold.

Sensitivity⬆️, Specificity⬇️ and Sensitivity⬇️, Specificity⬆️

TPR⬆️, FPR⬆️ and TPR⬇️, FPR⬇️

How to use the AUC ROC curve for the multi-class model?

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