I am new to Machine Learning and have been doing some practice on Logistic Regression. To evaluate the models, I've been trying to create some ROC plots. The package that i used is pROC.
The model name is - 'model' dataset is 'data'.
The code I used in R is:
library(pROC) predictionData <- predict(model, newdata = data, type = "response") rocModel <- roc(data$y ~ as.numeric(predictionData>0.5)) plot(rocModel)
Ideally, from what i have learned, ROC should plot Sensitivity or TPR (True Positive Rate) vs 1 - Specificity. But as shown in the picture below, it shows Sensitivity vs Specificity.
Am I missing some obvious trick here or is something wrong with what I have done ?