# R/gbm_bin.R In mob: Monotonic Optimal Binning

#### Documented in gbm_bin

```#' Monotonic binning based on generalized boosted model
#'
#' The function \code{gbm_bin} implements the monotonic binning based on
#' the generalized boosted model (GBM).
#'
#' @param x A numeric vector
#' @param y A numeric vector with 0/1 binary values
#'
#' @return A list of binning outcomes, including a numeric vector with cut
#'         points and a dataframe with binning summary
#'
#' @examples
#' data(hmeq)

gbm_bin <- function(x, y) {
x_ <- x[!is.na(x)]
y_ <- y[!is.na(x)]

spc <- cor(x_, y_, method = "spearman")

set.seed(1)
m_ <- gbm::gbm(y ~ x1 + x2, distribution = "bernoulli", data = data.frame(y = y_, x1 = x_, x2 = x_),
var.monotone = c(spc / abs(spc), spc / abs(spc)),
bag.fraction = 1, n.minobsinnode = round(length(x_) / 100), n.trees = 500)

d1 <- data.frame(y = y_, x = x_, cat = gbm::predict.gbm(m_, n.trees = m_\$n.trees, type = "response"))

l1 <- lapply(split(d1, d1\$cat),
function(d) list(rate = abs(round(mean(d\$y), 8)), maxx = max(d\$x)))

l2 <- l1[Reduce(c, lapply(l1, function(l) l\$rate > 0 & l\$rate < 1))]

l3 <- sort(Reduce(c, lapply(l2, function(l) l\$maxx)))[-length(l2)]

l4 <- manual_bin(x_, y_, l3)

return(list(cut = l3, tbl = gen_woe(add_miss(l4, x, y), l3)))
}
```

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mob documentation built on July 31, 2021, 9:06 a.m.