MLtools Package in R to Calculate AUC-ROC
The mltools package contains helper functions that help majorly in exploratory data analysis. The main objective behind this function is that it provides highly optimized functions for speed and memory. In the below code implementation, we can observe how easy it is to calculate auc() using the Metrics Package in R.
Installing and Loading the Libraries
Let’s first install the package using the install.packages() function and then we will load the same using the library() function in R.
R
install.packages("mltools")library(mltools) |
Initializing the Vectors
Now let’s create two imaginary vectors with the actual target values and the predicted probabilities for the respective classes.
R
# Actual Target variableactual <- c(0, 0, 1, 1, 1, 0, 0)# Predicted probabilities for # the respective classespredicted <- c(.1, .3, .4, .9, 0.76, 0.55, 0.2) |
Calculating AUC-ROC
Syntax:
auc_roc(actual, predicted, returnDT)
where,
- actual – The ground truth binary numeric vector containing 1 for the positive class and 0 for the negative class.
- predicted – A vector containing probabilities predicted by the model of each example being 1.
- returnDT – Returns a data.table object with False Positive Rate and True Positive Rate for plotting the ROC curve
R
auc_roc(predicted, actual) |
Output:
0.916666666666667
R
auc_roc(predicted, actual, returnDT=TRUE) |
Output:
TPR and FPR for the actual and predicted values
How to Calculate AUC (Area Under Curve) in R?
In this article, we will discuss how to calculate the AUC (Area under Curve) of the ROC (Receiver Operating Characteristic) curve in the R Programming Language.