wehub-resource-sync
58 lines
3.4 KiB
R
58 lines
3.4 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/lgb.Booster.R
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\name{lgb_predict_shared_params}
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\alias{lgb_predict_shared_params}
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\title{Shared prediction parameter docs}
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\arguments{
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\item{type}{Type of prediction to output. Allowed types are:\itemize{
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\item \code{"response"}: will output the predicted score according to the objective function being
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optimized (depending on the link function that the objective uses), after applying any necessary
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transformations - for example, for \code{objective="binary"}, it will output class probabilities.
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\item \code{"class"}: for classification objectives, will output the class with the highest predicted
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probability. For other objectives, will output the same as "response". Note that \code{"class"} is
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not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function
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for more details).
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\item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations'
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results) from which the "response" number is produced for a given objective function - for example,
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for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as
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"regression", since no transformation is applied, the output will be the same as for "response".
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\item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls
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in each tree in the model, outputted as integers, with one column per tree.
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\item \code{"contrib"}: will return the per-feature contributions for each prediction, including an
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intercept (each feature will produce one column).
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}
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Note that, if using custom objectives, types "class" and "response" will not be available and will
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default towards using "raw" instead.
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If the model was fit through function \link{lightgbm} and it was passed a factor as labels,
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passing the prediction type through \code{params} instead of through this argument might
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result in factor levels for classification objectives not being applied correctly to the
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resulting output.
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\emph{New in version 4.0.0}}
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\item{start_iteration}{int or None, optional (default=None)
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Start index of the iteration to predict.
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If None or <= 0, starts from the first iteration.}
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\item{num_iteration}{int or None, optional (default=None)
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Limit number of iterations in the prediction.
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If None, if the best iteration exists and start_iteration is None or <= 0, the
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best iteration is used; otherwise, all iterations from start_iteration are used.
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If <= 0, all iterations from start_iteration are used (no limits).}
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\item{params}{a list of additional named parameters. See
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\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{
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the "Predict Parameters" section of the documentation} for a list of parameters and
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valid values. Where these conflict with the values of keyword arguments to this function,
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the values in \code{params} take precedence.}
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}
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\description{
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Shared prediction parameter docs
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}
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\details{
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This page contains shared documentation for prediction-related parameters used throughout the package.
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}
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\keyword{internal}
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