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lgb.save

Save LightGBM model


Description

Save LightGBM model

Usage

lgb.save(booster, filename, num_iteration = NULL)

Arguments

booster

Object of class lgb.Booster

filename

saved filename

num_iteration

number of iteration want to predict with, NULL or <= 0 means use best iteration

Value

lgb.Booster

Examples

library(lightgbm)
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)
data(agaricus.test, package = "lightgbm")
test <- agaricus.test
dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
params <- list(objective = "regression", metric = "l2")
valids <- list(test = dtest)
model <- lgb.train(
  params = params
  , data = dtrain
  , nrounds = 10L
  , valids = valids
  , min_data = 1L
  , learning_rate = 1.0
  , early_stopping_rounds = 5L
)
lgb.save(model, tempfile(fileext = ".txt"))

lightgbm

Light Gradient Boosting Machine

v3.2.1
MIT + file LICENSE
Authors
Guolin Ke [aut, cre], Damien Soukhavong [aut], James Lamb [aut], Qi Meng [aut], Thomas Finley [aut], Taifeng Wang [aut], Wei Chen [aut], Weidong Ma [aut], Qiwei Ye [aut], Tie-Yan Liu [aut], Yachen Yan [ctb], Microsoft Corporation [cph], Dropbox, Inc. [cph], Jay Loden [cph], Dave Daeschler [cph], Giampaolo Rodola [cph], Alberto Ferreira [ctb], Daniel Lemire [ctb], Victor Zverovich [cph], IBM Corporation [ctb]
Initial release
2021-04-12

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