Logistic Regression is a statistical method that we use to fit a regression model when the response variable is binary. To assess how well a logistic regression model fits a dataset, we can look at the following two metrics:

**Sensitivity:**The probability that the model predicts a positive outcome for an observation when indeed the outcome is positive. This is also called the “true positive rate.”**Specificity:**The probability that the model predicts a negative outcome for an observation when indeed the outcome is negative. This is also called the “true negative rate.”

One way to visualize these two metrics is by creating a **ROC curve**, which stands for “receiver operating characteristic” curve.

This is a plot that displays the sensitivity along the y-axis and (1 – specificity) along the x-axis. One way to quantify how well the logistic regression model does at classifying data is to calculate **AUC**, which stands for “area under curve.”

The closer the AUC is to 1, the better the model.

The following step-by-step example shows how to calculate AUC for a logistic regression model in R.

**Step 1: Load the Data**

First, we’ll load the **Default** dataset from the **ISLR** package, which contains information about whether or not various individuals defaulted on a loan.

#load dataset data <- ISLR::Default #view first six rows of dataset head(data) default student balance income 1 No No 729.5265 44361.625 2 No Yes 817.1804 12106.135 3 No No 1073.5492 31767.139 4 No No 529.2506 35704.494 5 No No 785.6559 38463.496 6 No Yes 919.5885 7491.559

**Step 2: Fit the Logistic Regression Model**

Next, we’ll fit a logistic regression model to predict the probability that an individual defaults:

#make this example reproducible set.seed(1) #Use 70% of dataset as training set and remaining 30% as testing set sample <- sample(c(TRUE, FALSE), nrow(data), replace=TRUE, prob=c(0.7,0.3)) train <- data[sample, ] test <- data[!sample, ] #fit logistic regression model model <- glm(default~student+balance+income, family="binomial", data=train)

**Step 3: Calculate the AUC of the Model**

Next, we’ll use the **auc()** function from the **pROC** package to calculate the AUC of the model. This function uses the following syntax:

**auc(response, predicted)**

Here’s how to use this function in our example:

#calculate probability of default for each individual in test dataset predicted <- predict(model, test, type="response") #calculate AUC library(pROC) auc(test$default, predicted) Setting levels: control = No, case = Yes Setting direction: controls < cases Area under the curve: 0.9437

The AUC of the model turns out to be **0.9437**.

Since this value is close to 1, this indicates that the model does a very good job of predicting whether or not an individual will default on their loan.