R: Predict method for LightGBM model

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,
                   dtrain,
                   100,
                   valids,
                   min_data = 1,
                   learning_rate = 1,
                   early_stopping_rounds = 10)
preds <- predict(model, test$data)

[Package lightgbm version 2.2.2 Index]