52 lines
2.6 KiB
R
52 lines
2.6 KiB
R
% Generated by roxygen2 (4.1.0): do not edit by hand
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% Please edit documentation in R/xgb.importance.R
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\name{xgb.importance}
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\alias{xgb.importance}
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\title{Show importance of features in a model}
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\usage{
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xgb.importance(feature_names = NULL, filename_dump = NULL)
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}
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\arguments{
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\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.}
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\item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (\code{with.stats = T} in function \code{xgb.dump}).}
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}
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\value{
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A \code{data.table} of the features used in the model with their average gain (and their weight for boosted tree model) in the model.
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}
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\description{
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Read a xgboost model text dump.
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Can be tree or linear model (text dump of linear model are only supported in dev version of \code{Xgboost} for now).
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}
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\details{
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This is the function to understand the model trained (and through your model, your data).
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Results are returned for both linear and tree models.
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\code{data.table} is returned by the function.
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There are 3 columns :
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\itemize{
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\item \code{Features} name of the features as provided in \code{feature_names} or already present in the model dump.
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\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 ;
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\item \code{Cover} metric of the number of observation related to this feature (only available for tree models) ;
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\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.
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}
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}
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\examples{
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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#Both dataset are list with two items, a sparse matrix and labels (labels = outcome column which will be learned).
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#Each column of the sparse Matrix is a feature in one hot encoding format.
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train <- agaricus.train
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test <- agaricus.test
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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eta = 1, nround = 2,objective = "binary:logistic")
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xgb.dump(bst, 'xgb.model.dump', with.stats = T)
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#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
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xgb.importance(agaricus.test$data@Dimnames[[2]], 'xgb.model.dump')
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}
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