Polishing API + wording in function description #Rstat
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@@ -10,9 +10,8 @@
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#' @importFrom stringr str_detect
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#' @importFrom stringr str_extract
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#'
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#' @param filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).
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#' @param model dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.
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#' @param features.keep number of features to keep in each position of the multi tree.
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#' @param features.keep number of features to keep in each position of the multi trees.
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#' @param plot.width width in pixels of the graph to produce
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#' @param plot.height height in pixels of the graph to produce
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#'
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@@ -20,21 +19,23 @@
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#'
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#' @details
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#'
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#' This function tries to capture the complexity of gradient boosted tree ensembles
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#' This function tries to capture the complexity of gradient boosted tree ensemble
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#' in a cohesive way.
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#'
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#' The goal is to improve the interpretability of the model generally seen as black box.
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#' The function is dedicated to boosting applied to decision trees only.
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#'
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#' The purpose is to move from an ensemble of trees to a single tree only.
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#'
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#' It takes advantage of the fact that the shape of a binary tree is only defined by
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#' its deepness.
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#' Therefore in a boosting model, all trees have the same shape.
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#' its deepness (therefore in a boosting model, all trees have the same shape).
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#'
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#' Moreover, the trees tend to reuse the same features.
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#'
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#' The function will project each trees on one, and keep for each position the
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#' \code{features.keep} first features (based on Gain per feature).
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#' The function will project each tree on one, and keep for each position the
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#' \code{features.keep} first features (based on Gain per feature measure).
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#'
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#' This function is inspired from this blog post:
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#' This function is inspired by this blog post:
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#' \url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}
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#'
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#' @examples
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