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,
  ...
)

Arguments

object

Object of class lgb.Booster

data

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

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.

...

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

# \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") valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 5L , valids = valids , min_data = 1L , learning_rate = 1.0 )
#> [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001076 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) # }