39 lines
1.4 KiB
R
39 lines
1.4 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.}
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}
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\description{
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Read a xgboost model in text file format.
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Can be tree or linear model (text dump of linear model are only supported in dev version of Xgboost for now).
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}
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\details{
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Return a data.table of the features with their weight.
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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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