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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.Booster.R
\name{predict.xgb.Booster}
\alias{predict.xgb.Booster}
\alias{predict.xgb.Booster.handle}
\title{Predict method for eXtreme Gradient Boosting model}
\usage{
\method{predict}{xgb.Booster}(object, newdata, missing = NA,
  outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE,
  predcontrib = FALSE, reshape = FALSE, ...)

\method{predict}{xgb.Booster.handle}(object, ...)
}
\arguments{
\item{object}{Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}}

\item{newdata}{takes \code{matrix}, \code{dgCMatrix}, local data file or \code{xgb.DMatrix}.}

\item{missing}{Missing is only used when input is dense matrix. Pick a float value that represents
missing values in data (e.g., sometimes 0 or some other extreme value is used).}

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

\item{ntreelimit}{limit the number of model's trees or boosting iterations used in prediction (see Details).
It will use all the trees by default (\code{NULL} value).}

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

\item{predcontrib}{whether to return feature contributions to individual predictions instead (see Details).}

\item{reshape}{whether to reshape the vector of predictions to a matrix form when there are several 
prediction outputs per case. This option has no effect when \code{predleaf = TRUE}.}

\item{...}{Parameters passed to \code{predict.xgb.Booster}}
}
\value{
For regression or binary classification, it returns a vector of length \code{nrows(newdata)}.
For multiclass classification, either a \code{num_class * nrows(newdata)} vector or 
a \code{(nrows(newdata), num_class)} dimension matrix is returned, depending on 
the \code{reshape} value.

When \code{predleaf = TRUE}, the output is a matrix object with the 
number of columns corresponding to the number of trees.

When \code{predcontrib = TRUE} and it is not a multiclass setting, the output is a matrix object with
\code{num_features + 1} columns. The last "+ 1" column in a matrix corresponds to bias.
For a multiclass case, a list of \code{num_class} elements is returned, where each element is
such a matrix. The contribution values are on the scale of untransformed margin 
(e.g., for binary classification would mean that the contributions are log-odds deviations from bias).
}
\description{
Predicted values based on either xgboost model or model handle object.
}
\details{
Note that \code{ntreelimit} is not necessarily equal to the number of boosting iterations
and it is not necessarily equal to the number of trees in a model.
E.g., in a random forest-like model, \code{ntreelimit} would limit the number of trees.
But for multiclass classification, while there are multiple trees per iteration, 
\code{ntreelimit} limits the number of boosting iterations.