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refactor dump function to adapt to the new possibilities of exporting a String

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El Potaeto
2015-01-09 00:14:01 +01:00
parent 6fd8bbe71a
commit 3e1eea0eea
4 changed files with 21 additions and 9 deletions
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7
R-package/man/xgb.dump.Rd
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@@ -4,12 +4,12 @@
\alias{xgb.dump}
\title{Save xgboost model to text file}
\usage{
xgb.dump(model, fname, fmap = "", with.stats = FALSE)
xgb.dump(model, fname = NULL, fmap = "", with.stats = FALSE)
}
\arguments{
\item{model}{the model object.}
\item{fname}{the name of the binary file.}
\item{fname}{the name of the text file where to save the model. If not provided or set to \code{NULL} the function will return the model as a \code{character} vector.}
\item{fmap}{feature map file representing the type of feature.
Detailed description could be found at
@@ -23,6 +23,9 @@ for example Format.}
gain is the approximate loss function gain we get in each split;
cover is the sum of second order gradient in each node.}
}
\value{
if fname is not provided or set to \code{NULL} the function will return the model as a \code{character} vector. Otherwise it will return \code{TRUE}.
}
\description{
Save a xgboost model to text file. Could be parsed later.
}

2
R-package/man/xgb.importance.Rd
View File

@@ -27,7 +27,7 @@ Results are returned for both linear and tree models.
There are 3 columns :
\itemize{
\item \code{Features} name of the features as provided in \code{feature_names} or already present in the model dump.
\item \code{Gain} contribution of each feature to the model. For boosted tree model, each gain of each feature of each tree is taken into account, then average per feature to give a vision of the entire model. Highest percentage means most important feature regarding the \code{label} used for the training ;
\item \code{Gain} contribution of each feature to the model. For boosted tree model, each gain of each feature of each tree is taken into account, then average per feature to give a vision of the entire model. Highest percentage means important feature to predict the \code{label} used for the training ;
\item \code{Cover} metric of the number of observation related to this feature (only available for tree models) ;
\item \code{Weight} percentage representing the relative number of times a feature have been taken into trees. \code{Gain} should be prefered to search the most important feature. For boosted linear model, this column has no meaning.
}