predict.lgb.Booster.Rd

% 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,
  type = "response",
  start_iteration = NULL,
  num_iteration = NULL,
  header = FALSE,
  params = list(),
  ...
)
}
\arguments{
\item{object}{Object of class \code{lgb.Booster}}

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

               For sparse inputs, if predictions are only going to be made for a single row, it will be faster to
               use CSR format, in which case the data may be passed as either a single-row CSR matrix (class
               \code{dgRMatrix} from package \code{Matrix}) or as a sparse numeric vector (class
               \code{dsparseVector} from package \code{Matrix}).

               If single-row predictions are going to be performed frequently, it is recommended to
               pre-configure the model object for fast single-row sparse predictions through function
               \link{lgb.configure_fast_predict}.}

\item{type}{Type of prediction to output. Allowed types are:\itemize{
            \item \code{"response"}: will output the predicted score according to the objective function being
                  optimized (depending on the link function that the objective uses), after applying any necessary
                  transformations - for example, for \code{objective="binary"}, it will output class probabilities.
            \item \code{"class"}: for classification objectives, will output the class with the highest predicted
                  probability. For other objectives, will output the same as "response". Note that \code{"class"} is
                  not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function
                  for more details).
            \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations'
                  results) from which the "response" number is produced for a given objective function - for example,
                  for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as
                  "regression", since no transformation is applied, the output will be the same as for "response".
            \item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls
                  in each tree in the model, outputted as integers, with one column per tree.
            \item \code{"contrib"}: will return the per-feature contributions for each prediction, including an
                  intercept (each feature will produce one column).
            }

            Note that, if using custom objectives, types "class" and "response" will not be available and will
            default towards using "raw" instead.}

\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{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 prediction types that are meant to always return one output per observation (e.g. when predicting
        \code{type="response"} or \code{type="raw"} on a binary classification or regression objective), will
        return a vector with one element per row in \code{newdata}.

        For prediction types that are meant to return more than one output per observation (e.g. when predicting
        \code{type="response"} or \code{type="raw"} on a multi-class objective, or when predicting
        \code{type="leaf"}, regardless of objective), will return a matrix with one row per observation in
        \code{newdata} and one column per output.

        For \code{type="leaf"} predictions, will return a matrix with one row per observation in \code{newdata}
        and one column per tree. Note that for multiclass objectives, LightGBM trains one tree per class at each
        boosting iteration. That means that, for example, for a multiclass model with 3 classes, the leaf
        predictions for the first class can be found in columns 1, 4, 7, 10, etc.

        For \code{type="contrib"}, will return a matrix of SHAP values with one row per observation in
        \code{newdata} and columns corresponding to features. For regression, ranking, cross-entropy, and binary
        classification objectives, this matrix contains one column per feature plus a final column containing the
        Shapley base value. For multiclass objectives, this matrix will represent \code{num_classes} such matrices,
        in the order "feature contributions for first class, feature contributions for second class, feature
        contributions for third class, etc.".
}
\description{
Predicted values based on class \code{lgb.Booster}
}
\details{
If the model object has been configured for fast single-row predictions through
         \link{lgb.configure_fast_predict}, this function will use the prediction parameters
         that were configured for it - as such, extra prediction parameters should not be passed
         here, otherwise the configuration will be ignored and the slow route will be taken.
}
\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
   )
)
}
}