Created
October 25, 2021 20:26
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Lift to AUC
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| #' Lift to AUC | |
| #' | |
| #' @param percentiles | |
| #' @param conversion_rate | |
| #' | |
| #' @return list | |
| #' | |
| #' @description | |
| #' | |
| #' This function takes in percentiles and the conversion rate, | |
| #' and computes the AUC. | |
| #' | |
| #' Often we see tables that have groups and conversion rate (or relative | |
| #' to a baseline) and it's a report of model performance. However, it's | |
| #' difficult to reason about. | |
| #' | |
| #' This function converts that to AUC, and reports Gini on the way. | |
| #' | |
| #' @examples | |
| #' | |
| #' x <- rbeta(10, 1, 9) | |
| #' percentiles <- cumsum(x) / sum(x) | |
| #' conversion_rate <- rbeta(10, 1, 100) | |
| #' | |
| #' lift_to_auc(percentiles, conversion_rate) | |
| #' | |
| lift_to_auc <- function(percentiles, conversion_rate) { | |
| sizes <- diff(c(0, percentiles)) | |
| cume_convs <- cumsum(sizes * conversion_rate) / sum(sizes * conversion_rate) | |
| breaks <- ((cume_convs + na.exclude(lag(c(0, cume_convs)))) / 2) * sizes | |
| gini <- sum(breaks) | |
| auc <- (1+gini) / 2 | |
| return(list( | |
| Gini = gini, | |
| AUC = auc | |
| )) | |
| } |
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