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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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14
R-package/man/xgb.plot.importance.Rd
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@@ -17,13 +17,13 @@ xgb.plot.importance(importance_matrix = NULL, top_n = NULL,
\item{top_n}{maximal number of top features to include into the plot.}
\item{measure}{the name of importance measure to plot.
\item{measure}{the name of importance measure to plot.
When \code{NULL}, 'Gain' would be used for trees and 'Weight' would be used for gblinear.}
\item{rel_to_first}{whether importance values should be represented as relative to the highest ranked feature.
See Details.}
\item{n_clusters}{(ggplot only) a \code{numeric} vector containing the min and the max range
\item{n_clusters}{(ggplot only) a \code{numeric} vector containing the min and the max range
of the possible number of clusters of bars.}
\item{...}{other parameters passed to \code{barplot} (except horiz, border, cex.names, names.arg, and las).}
@@ -33,7 +33,7 @@ When it is NULL, the existing \code{par('mar')} is used.}
\item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{barplot}.}
\item{plot}{(base R barplot) whether a barplot should be produced.
\item{plot}{(base R barplot) whether a barplot should be produced.
If FALSE, only a data.table is returned.}
}
\value{
@@ -53,14 +53,14 @@ Features are shown ranked in a decreasing importance order.
It works for importances from both \code{gblinear} and \code{gbtree} models.
When \code{rel_to_first = FALSE}, the values would be plotted as they were in \code{importance_matrix}.
For gbtree model, that would mean being normalized to the total of 1
For gbtree model, that would mean being normalized to the total of 1
("what is feature's importance contribution relative to the whole model?").
For linear models, \code{rel_to_first = FALSE} would show actual values of the coefficients.
Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of
Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of
"what is feature's importance contribution relative to the most important feature?"
The ggplot-backend method also performs 1-D custering of the importance values,
with bar colors coresponding to different clusters that have somewhat similar importance values.
The ggplot-backend method also performs 1-D clustering of the importance values,
with bar colors corresponding to different clusters that have somewhat similar importance values.
}
\examples{
data(agaricus.train)