Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

lgb.dump

Dump LightGBM model to json


Description

Dump LightGBM model to json

Usage

lgb.dump(booster, num_iteration = NULL)

Arguments

booster

Object of class lgb.Booster

num_iteration

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

Value

json format of model

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
)
json_model <- lgb.dump(model)

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

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.