predict.lgb.Booster: Predict method for LightGBM model

In lightgbm: Light Gradient Boosting Machine

Predict method for LightGBM model

Description

Predicted values based on class lgb.Booster

Usage

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

Arguments

object	Object of class lgb.Booster
data	a matrix object, a dgCMatrix object or a character representing a path to a text file (CSV, TSV, or LibSVM)
start_iteration	int or None, optional (default=None) Start index of the iteration to predict. If None or <= 0, starts from the first iteration.
num_iteration	int or None, optional (default=None) Limit number of iterations in the prediction. If None, if the best iteration exists and start_iteration is None or <= 0, the best iteration is used; otherwise, all iterations from start_iteration are used. If <= 0, all iterations from start_iteration are used (no limits).
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.
params	a list of additional named parameters. See the "Predict Parameters" section of the documentation for a list of parameters and valid values.
...	Additional prediction parameters. NOTE: deprecated as of v3.3.0. Use params instead.

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

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"
  , min_data = 1L
  , learning_rate = 1.0
)
valids <- list(test = dtest)
model <- lgb.train(
  params = params
  , data = dtrain
  , nrounds = 5L
  , valids = valids
)
preds <- predict(model, test$data)

# pass other prediction parameters
preds <- predict(
    model,
    test$data,
    params = list(
        predict_disable_shape_check = TRUE
   )
)

lightgbm documentation built on Jan. 17, 2023, 1:13 a.m.