Predicted values based on class lgb.Booster

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

Arguments

object

Object of class lgb.Booster

newdata

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

params

a list of additional named parameters. See the "Predict Parameters" section of the documentation for a list of parameters and valid values. Where these conflict with the values of keyword arguments to this function, the values in params take precedence.

...

ignored

Value

For regression or binary classification, it returns a vector of length nrows(data). For multiclass classification, it returns a matrix of dimensions (nrows(data), num_class).

When passing predleaf=TRUE or predcontrib=TRUE, the output will always be returned as a matrix.

Examples

# \donttest{
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
)
#> [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000947 seconds.
#> You can set `force_row_wise=true` to remove the overhead.
#> And if memory is not enough, you can set `force_col_wise=true`.
#> [LightGBM] [Info] Total Bins 232
#> [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
#> [LightGBM] [Info] Start training from score 0.482113
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [1] "[1]:  test's l2:6.44165e-17"
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [1] "[2]:  test's l2:1.97215e-31"
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [1] "[3]:  test's l2:0"
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
#> [1] "[4]:  test's l2:0"
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
#> [1] "[5]:  test's l2:0"
preds <- predict(model, test$data)

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