Predicted values based on class lgb.Booster

# S3 method for 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

# \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 0.000954 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 ) ) # }