refactoring of importance function
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@@ -27,7 +27,8 @@ Results are returned for both linear and tree models.
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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{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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@@ -4,7 +4,7 @@
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\alias{xgb.model.dt.tree}
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\title{Convert tree model dump to data.table}
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\usage{
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xgb.model.dt.tree(feature_names = NULL, filename_dump = NULL,
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xgb.model.dt.tree(feature_names = NULL, filename_dump = NULL, text = NULL,
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n_first_tree = NULL)
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}
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\arguments{
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