Predict method for LightGBM model

Predicted values based on class lgb.Booster

# S3 method for 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)
#> [1]:	test's l2:6.44165e-17 
#> [2]:	test's l2:6.44165e-17 
#> [3]:	test's l2:6.44165e-17 
#> [4]:	test's l2:6.44165e-17 
#> [5]:	test's l2:6.44165e-17 
#> [6]:	test's l2:6.44165e-17 
#> [7]:	test's l2:6.44165e-17 
#> [8]:	test's l2:6.44165e-17 
#> [9]:	test's l2:6.44165e-17 
#> [10]:	test's l2:6.44165e-17 
#> [11]:	test's l2:6.44165e-17 
preds <- predict(model, test$data)