% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lgb.Booster.R
\name{predict.lgb.Booster}
\alias{predict.lgb.Booster}
\title{Predict method for LightGBM model}
\usage{
\method{predict}{lgb.Booster}(
  object,
  newdata,
  start_iteration = NULL,
  num_iteration = NULL,
  rawscore = FALSE,
  predleaf = FALSE,
  predcontrib = FALSE,
  header = FALSE,
  params = list(),
  ...
)
}
\arguments{
\item{object}{Object of class \code{lgb.Booster}}

\item{newdata}{a \code{matrix} object, a \code{dgCMatrix} object or
a character representing a path to a text file (CSV, TSV, or LibSVM)}

\item{start_iteration}{int or None, optional (default=None)
Start index of the iteration to predict.
If None or <= 0, starts from the first iteration.}

\item{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).}

\item{rawscore}{whether the prediction should be returned in the for of original untransformed
sum of predictions from boosting iterations' results. E.g., setting \code{rawscore=TRUE}
for logistic regression would result in predictions for log-odds instead of probabilities.}

\item{predleaf}{whether predict leaf index instead.}

\item{predcontrib}{return per-feature contributions for each record.}

\item{header}{only used for prediction for text file. True if text file has header}

\item{params}{a list of additional named parameters. See
\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{
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 \code{params} take precedence.}

\item{...}{ignored}
}
\value{
For regression or binary classification, it returns a vector of length \code{nrows(data)}.
        For multiclass classification, it returns a matrix of dimensions \code{(nrows(data), num_class)}.

        When passing \code{predleaf=TRUE} or \code{predcontrib=TRUE}, the output will always be
        returned as a matrix.
}
\description{
Predicted values based on class \code{lgb.Booster}
}
\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
)
preds <- predict(model, test$data)

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