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Small rewording function xgb.importance

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pommedeterresautee 2015-12-02 15:48:22 +01:00
parent 6ceb3438be
commit edca27fa32
2 changed files with 16 additions and 10 deletions
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13
R-package/R/xgb.importance.R
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@ -25,14 +25,17 @@
#' Results are returned for both linear and tree models.
#'
#' \code{data.table} is returned by the function.
#' There are 3 columns :
#' The columns are :
#' \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 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.
#' \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 important feature to predict the \code{label} used for the training (only available for tree models);
#' \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.
#' }
#'
#' If you don't provide name, index of the features are used.
#' They are extracted from the boost dump (made on the C++ side), the index starts at 0 (usual in C++) instead of 1 (usual in R).
#'
#' Co-occurence count
#' ------------------
#'

13
R-package/man/xgb.importance.Rd
View File

@ -31,14 +31,17 @@ This is the function to understand the model trained (and through your model, yo
Results are returned for both linear and tree models.
\code{data.table} is returned by the function.
There are 3 columns :
The columns are :
\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 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.
\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 important feature to predict the \code{label} used for the training (only available for tree models);
\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.
}
If you don't provide name, index of the features are used.
They are extracted from the boost dump (made on the C++ side), the index starts at 0 (usual in C++) instead of 1 (usual in R).
Co-occurence count
------------------