wrap_lightgbm: S3 wrapper for lightgbm

In mlS3: Unified S3 Interface to Machine Learning Models

S3 wrapper for lightgbm

Description

Fits a 'lightgbm' model with a consistent interface. Supports binary classification, multiclass classification, and regression.

Usage

wrap_lightgbm(x, y, ...)

## S3 method for class 'wrap_lightgbm'
predict(object, newx, type = c("class", "prob"), ...)

## S3 method for class 'wrap_lightgbm'
print(x, ...)

Arguments

x	A matrix or data.frame of features.
y	A factor or character vector for classification, numeric for regression.
...	Additional arguments passed to [lightgbm::lgb.train()]. Pass 'params = list(objective = "binary")' for binary classification, 'params = list(objective = "multiclass", num_class = k)' for multiclass, or 'params = list(objective = "regression")' for regression.
object	A fitted 'wrap_lightgbm' object.
newx	A matrix or data.frame of new observations.
type	'"class"' (default) for class labels, '"prob"' for a probability matrix. Ignored for regression.

Value

An object of class 'wrap_lightgbm' with fields:

fit	The fitted lgb.Booster model.
levels	Class levels (NULL for regression).
task	"classification" or "regression".
objective	The lightgbm objective string, stored at fit time.

Examples

library(mlS3)

X <- iris[, 1:4]
y <- iris$Species
mod <- wrap_lightgbm(X, y,
  params = list(objective = "multiclass", num_class = 3, verbose = -1),
  nrounds = 50)
predict(mod, newx = X, type = "class")
predict(mod, newx = X, type = "prob")

mlS3 documentation built on April 29, 2026, 1:08 a.m.