Find beta shape parameters matching the conditional distributions.

Source: R/ROC.R

Based on https://win-vector.com/2020/09/13/why-working-with-auc-is-more-powerful-than-one-might-think/. Used to find one beta distribution on positive examples, and another on negative examples.

find_matching_conditional_betas(modelPredictions, yValues, ..., yTarget = TRUE)

find_ROC_matching_ab(modelPredictions, yValues, ..., yTarget = TRUE)

Arguments

modelPredictions

numeric predictions (not empty), ordered (either increasing or decreasing)
yValues

truth values (not empty, same length as model predictions)
...

force later arguments to bind by name.
yTarget

value considered to be positive.

Value

beta curve shape parameters

Examples


d <- rbind(
  data.frame(x = rbeta(1000, shape1 = 6, shape2 = 4), y = TRUE),
  data.frame(x = rbeta(1000, shape1 = 2, shape2 = 3), y = FALSE)
)
find_matching_conditional_betas(modelPredictions = d$x, yValues = d$y)

#> shape1_pos shape2_pos shape1_neg shape2_neg 
#>   6.364140   4.189832   1.937121   2.969809 
# should be near
# shape1_pos shape2_pos shape1_neg shape2_neg
# 6          4          2          3
#
# # How to land all as variables
# unpack[shape1_pos, shape2_pos, shape1_neg, shape2_neg] <-
#    find_ROC_matching_ab(modelPredictions = d$x, yValues = d$y)