predict.lgb.Booster {lightgbm}R Documentation

Predict method for LightGBM model

Description

Predicted values based on class lgb.Booster

Usage

## S3 method for class 'lgb.Booster'
predict(
  object,
  data,
  num_iteration = NULL,
  rawscore = FALSE,
  predleaf = FALSE,
  predcontrib = FALSE,
  header = FALSE,
  reshape = FALSE,
  ...
)

Arguments

object

Object of class lgb.Booster

data

a matrix object, a dgCMatrix object or a character representing a filename

num_iteration

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

rawscore

whether the prediction should be returned in the for of original untransformed sum of predictions from boosting iterations' results. E.g., setting rawscore=TRUE for logistic regression would result in predictions for log-odds instead of probabilities.

predleaf

whether predict leaf index instead.

predcontrib

return per-feature contributions for each record.

header

only used for prediction for text file. True if text file has header

reshape

whether to reshape the vector of predictions to a matrix form when there are several prediction outputs per case.

...

Additional named arguments passed to the predict() method of the lgb.Booster object passed to object.

Value

For regression or binary classification, it returns a vector of length nrows(data). For multiclass classification, either a num_class * nrows(data) vector or a (nrows(data), num_class) dimension matrix is returned, depending on the reshape value.

When predleaf = TRUE, the output is a matrix object with the number of columns corresponding to the number of trees.

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
)
preds <- predict(model, test$data)
[Package lightgbm version 2.3.1 Index]