| lgb.save {lightgbm} | R Documentation |
Save LightGBM model
lgb.save(booster, filename, num_iteration = NULL)
booster |
Object of class |
filename |
saved filename |
num_iteration |
number of iteration want to predict with, NULL or <= 0 means use best iteration |
lgb.Booster
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,
dtrain,
100,
valids,
min_data = 1,
learning_rate = 1,
early_stopping_rounds = 10)
lgb.save(model, "model.txt")