Calculate AUC: Area Under The ROC Curve Using sklearn in Python

by | Jan 13, 2023 | AI, Machine Learning and Deep Learning, Machine Learning Using Python, Python Scikit-learn

Area Under The ROC Curve

What is an ROC Curve?

An ROC curve plots the True Positive Rate (TPR) vs. the False Positive Rate (FPR) at various classification thresholds. Before we understand the ROC curve clearly, let’s try to understand a few terms.

Let’s say the expected or actual output for a classification problem is:

Ya = [False, False, True, True, True, False, True, False, False, True]

And the calculated or predicted output is:

Y = [True, False, True, True, False, False, True, False, False, True]

True Positives (TP): True positives are those output labels that are predicted to be True, and they are actually True.

True Negatives (TN): True negatives are the output labels that are predicted to be False, and they are actually False also.

False Positives (FP): False positives are the output labels that are predicted to be True, but they are actually False.

False Negatives (FN): False negatives are those output labels that are predicted to be False, but they are actually True.

Now, the True Positive Rate (TPR) or Recall is calculated as follows:

TPR=\frac{TP}{TP+FN}

And the False Positive Rate is calculated as follows:

FPR=\frac{FP}{TN+FP}

Please note that for a classification problem, the specificity or True Negative Rate is defined as:

Specificity\ or\ TNR=\frac{TN}{TN+FP}=1-FPR

How to calculate the Area Under the ROC Curve using sklearn in Python?

AUC or Area Under the ROC Curve measures the area under the ROC curve. It is a value from 0 to 1. If predictions are 100% …

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Amrita Mitra

Author

Ms. Amrita Mitra is an author, who has authored the books “Cryptography And Public Key Infrastructure“, “Web Application Vulnerabilities And Prevention“, “A Guide To Cyber Security” and “Phishing: Detection, Analysis And Prevention“. She is also the founder of Asigosec Technologies, the company that owns The Security Buddy.

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