Monotonic Binning Based on Isotonic Regression

isoreg_bin.r

isoreg_bin <- function(data, y, x) {
  n1 <- 50
  n2 <- 10
  yname <- deparse(substitute(y))
  xname <- deparse(substitute(x))
  df1 <- data[, c(yname, xname)]
  df2 <- df1[!is.na(df1[, xname]), c(xname, yname)]
  cor <- cor(df2[, 2], df2[, 1], method = "spearman", use = "complete.obs")
  reg <- isoreg(df2[, 1], cor / abs(cor) * df2[, 2])
  cut <- knots(as.stepfun(reg))
  df2$cut <- cut(df2[[xname]], breaks = unique(cut), include.lowest = T)
  df3 <- Reduce(rbind,
                lapply(split(df2, df2$cut),
                       function(x) data.frame(n = nrow(x),
                                              b = sum(x[[yname]]),
                                              g = sum(1 - x[[yname]]),
                                              maxx = max(x[[xname]]),
                                              minx = min(x[[xname]]))))
  df4 <- df3[which(df3[["n"]] > n1 & df3[["b"]] > n2 & df3[["g"]] > n2), ]
  df1$good <- 1 -  df1[[yname]]
  return(smbinning::smbinning.custom(df1, "good", xname, cuts = df4$maxx[-nrow(df4)])$ivtable)
}

df <- sas7bdat::read.sas7bdat("Downloads/accepts.sas7bdat")

isoreg_bin(df, bad, bureau_score)

Monotonic binning is an important algorithm used in the scorecard development of consumer credit risk. The R function isoreg_bin() implements the monotonic binning based on the isotonic regression.