# ROC and Area Under Curve in Data Mining

## ROC and Area Under Curve in Data Mining

“ROC and Area Under Curve in Data Mining” is the topic of discussion in this tutorial. Let’s begin.

## What is ROC Curve?

ROC Curve is a tool helpful when predicting the probability of a binary outcome is the ROC curve. ROC stands for Receiver Operating Characteristic curve.

It can be the plot to compare the false positive rate on the x-axis versus the true positive rate on the y-axis.

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## How to calculate the True Positive Rate?

True Positive Rate can be calculated by the following formulae;

True Positive Rate = True Positives / (True Positives + False Negatives)

How to calculate Sensitivity?

## How to calculate the sensitivity?

Sensitivity can be calculated by the following formulae;

Sensitivity = True Positives / (True Positives + False Negatives)

## How to calculate the False Positive Rate?

False Positive Rate can be calculated by the following formulae;

False Positive Rate = False Positives / (False Positives + True Negatives)

## How to calculate Specificity?

Specificity can be calculated by the following formulae;

Specificity = True Negatives / (True Negatives + False Positives) where False Positive Rate = 1 – Specificity