auc: Area under curve of predicted binary response
In StatMeasures: Easy Data Manipulation, Data Quality and Statistical Checks

Description Usage Arguments Details Value Author(s) See Also Examples

Takes in actual binary response and predicted probabilities, and returns auc value

1	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

accuracy, ks, iv, gini, splitdata

# 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'])