Area under curve of predicted binary response
Takes in actual binary response and predicted probabilities, and returns auc value
auc(y, yhat)
y |
actual binary response |
yhat |
predicted probabilities corresponding to the actual binary response |
Area under the receiver operating characteristic (ROC) curve is the most sought after criteria for judging how good model predictions are.
auc function calculates the true positive rates (TPR) and false positive
rates (FPR) for each cutoff from 0.01 to 1 and calculates the area using trapezoidal
approximation. A ROC curve is also generated.
area under the ROC curve
Akash Jain
# A 'data.frame' with y and yhat
df <- data.frame(y = c(1, 0, 1, 1, 0, 0, 1, 0, 1, 0),
yhat = c(0.86, 0.23, 0.65, 0.92, 0.37, 0.45, 0.72, 0.19, 0.92, 0.50))
# AUC figure
AUC <- auc(y = df[, 'y'], yhat = df[, 'yhat'])Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.