Predict method for LightGBM model
Predicted values based on class lgb.Booster
## 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, ... )
object |
Object of class |
data |
a |
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 |
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 |
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.
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 ) preds <- predict(model, test$data)
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