61 lines
2.2 KiB
R
61 lines
2.2 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/xgb.plot.multi.trees.R
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\name{xgb.plot.multi.trees}
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\alias{xgb.plot.multi.trees}
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\title{Project all trees on one tree and plot it}
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\usage{
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xgb.plot.multi.trees(model, feature_names = NULL, features_keep = 5,
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plot_width = NULL, plot_height = NULL, ...)
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}
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\arguments{
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\item{model}{dump generated by the \code{xgb.train} function.}
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\item{feature_names}{names of each feature as a \code{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{features_keep}{number of features to keep in each position of the multi trees.}
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\item{plot_width}{width in pixels of the graph to produce}
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\item{plot_height}{height in pixels of the graph to produce}
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\item{...}{currently not used}
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}
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\value{
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Two graphs showing the distribution of the model deepness.
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}
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\description{
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Visualization of the ensemble of trees as a single collective unit.
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}
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\details{
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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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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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The purpose is to move from an ensemble of trees to a single tree only.
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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 (therefore in a boosting model, all trees have the same shape).
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Moreover, the trees tend to reuse the same features.
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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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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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data(agaricus.train, package='xgboost')
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bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
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eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
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min_child_weight = 50)
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p <- xgb.plot.multi.trees(model = bst, feature_names = colnames(agaricus.train$data), features_keep = 3)
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print(p)
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}
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