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Classification

objective = "binary" fits a logistic classification model. The response must be 0/1 (numeric or logical) or a two-level factor.

library(fastgbm)

x <- as.matrix(mtcars[, c("mpg", "disp", "hp", "wt")])
y <- mtcars$am  # 0 = automatic, 1 = manual

fit <- fastgbm(
  x, y = y, objective = "binary",
  ntrees = 100L, learning_rate = 0.1, max_depth = 3L,
  seed = 1L, verbose = FALSE
)
fit
#> fastgbm model
#>   objective: binary 
#>   trees: 100 
#>   learning rate: 0.1 
#>   max depth: 3

objective can be omitted: fastgbm() defaults to "binary" whenever y is a 0/1 vector (or two-level factor).

Predictions and evaluation

prob <- predict(fit, x, type = "response")  # predicted probabilities
head(prob)
#> [1] 0.93921700 0.92192505 0.84997064 0.05151746 0.07929779 0.03379528

link <- predict(fit, x, type = "link")      # log-odds
head(link)
#> [1]  2.737736  2.468795  1.734371 -2.912943 -2.451926 -3.353055

metrics(fit, y = y)  # log loss
#> $objective
#> [1] "binary"
#> 
#> $metric
#> [1] "logloss"
#> 
#> $value
#> [1] 0.2412709
mean((prob > 0.5) == y)  # training accuracy
#> [1] 0.9375
importance(fit)
#>   feature      gain
#> 4      wt 50.837932
#> 2    disp  8.623295
#> 3      hp  3.579048
#> 1     mpg  2.740842

Formula interface

dat <- mtcars
dat$am <- factor(dat$am)
fit2 <- fastgbm(am ~ mpg + disp + hp + wt, data = dat, ntrees = 100L, verbose = FALSE)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.