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, ...)



Object of class lgb.Booster


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


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


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.


whether predict leaf index instead.


return per-feature contributions for each record.


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


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.


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.


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 )
#> [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
preds <- predict(model, test$data)