wehub-resource-sync
116 lines
6.1 KiB
R
116 lines
6.1 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/lightgbm.R
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\name{lgb_shared_params}
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\alias{lgb_shared_params}
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\title{Shared parameter docs}
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\arguments{
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\item{callbacks}{List of callback functions that are applied at each iteration.}
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\item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}},
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may allow you to pass other types of data like \code{matrix} and then separately supply
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\code{label} as a keyword argument.}
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\item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null,
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training will stop if the evaluation of any metric on any validation set
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fails to improve for \code{early_stopping_rounds} consecutive boosting rounds.
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If training stops early, the returned model will have attribute \code{best_iter}
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set to the iteration number of the best iteration.}
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\item{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of
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strings and functions.
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\itemize{
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\item{\bold{a. character vector}:
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If you provide a character vector to this argument, it should contain strings with valid
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evaluation metrics.
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See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{
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The "metric" section of the documentation}
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for a list of valid metrics.
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}
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\item{\bold{b. function}:
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You can provide a custom evaluation function. This
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should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named
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list with three elements:
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\itemize{
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\item{\code{name}: A string with the name of the metric, used for printing
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and storing results.
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}
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\item{\code{value}: A single number indicating the value of the metric for the
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given predictions and true values
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}
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\item{
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\code{higher_better}: A boolean indicating whether higher values indicate a better fit.
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For example, this would be \code{FALSE} for metrics like MAE or RMSE.
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}
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}
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}
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\item{\bold{c. list}:
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If a list is given, it should only contain character vectors and functions.
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These should follow the requirements from the descriptions above.
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}
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}}
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\item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided}
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\item{init_model}{path of model file or \code{lgb.Booster} object, will continue training from this model}
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\item{nrounds}{number of training rounds}
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\item{obj}{objective function, can be character or custom objective function. Examples include
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\code{regression}, \code{regression_l1}, \code{huber},
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\code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}}
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\item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{
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the "Parameters" section of the documentation} for a list of parameters and valid values.}
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\item{verbose}{verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the
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printing of evaluation during training}
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\item{serializable}{whether to make the resulting objects serializable through functions such as
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\code{save} or \code{saveRDS} (see section "Model serialization").}
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}
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\description{
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Parameter docs shared by \code{lgb.train}, \code{lgb.cv}, and \code{lightgbm}
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}
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\section{Early Stopping}{
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"early stopping" refers to stopping the training process if the model's performance on a given
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validation set does not improve for several consecutive iterations.
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If multiple arguments are given to \code{eval}, their order will be preserved. If you enable
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early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all
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metrics will be considered for early stopping.
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If you want to only consider the first metric for early stopping, pass
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\code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric}
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in \code{params}, that metric will be considered the "first" one. If you omit \code{metric},
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a default metric will be used based on your choice for the parameter \code{obj} (keyword argument)
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or \code{objective} (passed into \code{params}).
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\bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored
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and early stopping will not be performed.
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}
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\section{Model serialization}{
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LightGBM model objects can be serialized and de-serialized through functions such as \code{save}
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or \code{saveRDS}, but similarly to libraries such as 'xgboost', serialization works a bit differently
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from typical R objects. In order to make models serializable in R, a copy of the underlying C++ object
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as serialized raw bytes is produced and stored in the R model object, and when this R object is
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de-serialized, the underlying C++ model object gets reconstructed from these raw bytes, but will only
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do so once some function that uses it is called, such as \code{predict}. In order to forcibly
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reconstruct the C++ object after deserialization (e.g. after calling \code{readRDS} or similar), one
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can use the function \link{lgb.restore_handle} (for example, if one makes predictions in parallel or in
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forked processes, it will be faster to restore the handle beforehand).
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Producing and keeping these raw bytes however uses extra memory, and if they are not required,
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it is possible to avoid producing them by passing `serializable=FALSE`. In such cases, these raw
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bytes can be added to the model on demand through function \link{lgb.make_serializable}.
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\emph{New in version 4.0.0}
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}
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\keyword{internal}
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