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fixed typos in R package docs (#4345)

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* fixed typos in R package docs

* updated verbosity parameter in xgb.train docs
This commit is contained in:
James Lamb
2019-04-21 02:54:11 -05:00
committed by Jiaming Yuan
parent 65db8d0626
commit 5e97de6a41
30 changed files with 414 additions and 413 deletions
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48
R-package/man/xgb.cv.Rd
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@@ -16,7 +16,7 @@ xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
\itemize{
\item \code{objective} objective function, common ones are
\itemize{
\item \code{reg:squarederror} Regression with squared loss.
\item \code{reg:squarederror} Regression with squared loss
\item \code{binary:logistic} logistic regression for classification
}
\item \code{eta} step size of each boosting step
@@ -35,11 +35,11 @@ xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
\item{label}{vector of response values. Should be provided only when data is an R-matrix.}
\item{missing}{is only used when input is a dense matrix. By default is set to NA, which means
that NA values should be considered as 'missing' by the algorithm.
\item{missing}{is only used when input is a dense matrix. By default is set to NA, which means
that NA values should be considered as 'missing' by the algorithm.
Sometimes, 0 or other extreme value might be used to represent missing values.}
\item{prediction}{A logical value indicating whether to return the test fold predictions
\item{prediction}{A logical value indicating whether to return the test fold predictions
from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.}
\item{showsd}{\code{boolean}, whether to show standard deviation of cross validation}
@@ -56,28 +56,28 @@ from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callb
\item \code{merror} Exact matching error, used to evaluate multi-class classification
}}
\item{obj}{customized objective function. Returns gradient and second order
\item{obj}{customized objective function. Returns gradient and second order
gradient with given prediction and dtrain.}
\item{feval}{custimized evaluation function. Returns
\code{list(metric='metric-name', value='metric-value')} with given
\item{feval}{customized evaluation function. Returns
\code{list(metric='metric-name', value='metric-value')} with given
prediction and dtrain.}
\item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified
\item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified
by the values of outcome labels.}
\item{folds}{\code{list} provides a possibility to use a list of pre-defined CV folds
(each element must be a vector of test fold's indices). When folds are supplied,
(each element must be a vector of test fold's indices). When folds are supplied,
the \code{nfold} and \code{stratified} parameters are ignored.}
\item{verbose}{\code{boolean}, print the statistics during the process}
\item{print_every_n}{Print each n-th iteration evaluation messages when \code{verbose>0}.
Default is 1 which means all messages are printed. This parameter is passed to the
Default is 1 which means all messages are printed. This parameter is passed to the
\code{\link{cb.print.evaluation}} callback.}
\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered.
If set to an integer \code{k}, training with a validation set will stop if the performance
\item{early_stopping_rounds}{If \code{NULL}, the early stopping function is not triggered.
If set to an integer \code{k}, training with a validation set will stop if the performance
doesn't improve for \code{k} rounds.
Setting this parameter engages the \code{\link{cb.early.stop}} callback.}
@@ -87,8 +87,8 @@ When it is \code{TRUE}, it means the larger the evaluation score the better.
This parameter is passed to the \code{\link{cb.early.stop}} callback.}
\item{callbacks}{a list of callback functions to perform various task during boosting.
See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
parameters' values. User can provide either existing or their own callback methods in order
See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
parameters' values. User can provide either existing or their own callback methods in order
to customize the training process.}
\item{...}{other parameters to pass to \code{params}.}
@@ -97,26 +97,26 @@ to customize the training process.}
An object of class \code{xgb.cv.synchronous} with the following elements:
\itemize{
\item \code{call} a function call.
\item \code{params} parameters that were passed to the xgboost library. Note that it does not
\item \code{params} parameters that were passed to the xgboost library. Note that it does not
capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
\item \code{callbacks} callback functions that were either automatically assigned or
\item \code{callbacks} callback functions that were either automatically assigned or
explicitly passed.
\item \code{evaluation_log} evaluation history storead as a \code{data.table} with the
first column corresponding to iteration number and the rest corresponding to the
\item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
first column corresponding to iteration number and the rest corresponding to the
CV-based evaluation means and standard deviations for the training and test CV-sets.
It is created by the \code{\link{cb.evaluation.log}} callback.
\item \code{niter} number of boosting iterations.
\item \code{nfeatures} number of features in training data.
\item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
\item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
parameter or randomly generated.
\item \code{best_iteration} iteration number with the best evaluation metric value
(only available with early stopping).
\item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
\item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
which could further be used in \code{predict} method
(only available with early stopping).
\item \code{pred} CV prediction values available when \code{prediction} is set.
\item \code{pred} CV prediction values available when \code{prediction} is set.
It is either vector or matrix (see \code{\link{cb.cv.predict}}).
\item \code{models} a liost of the CV folds' models. It is only available with the explicit
\item \code{models} a liost of the CV folds' models. It is only available with the explicit
setting of the \code{cb.cv.predict(save_models = TRUE)} callback.
}
}
@@ -124,9 +124,9 @@ An object of class \code{xgb.cv.synchronous} with the following elements:
The cross validation function of xgboost
}
\details{
The original sample is randomly partitioned into \code{nfold} equal size subsamples.
The original sample is randomly partitioned into \code{nfold} equal size subsamples.
Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.