96 lines
4.1 KiB
R
96 lines
4.1 KiB
R
% Generated by roxygen2: 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{Importance of features in a model.}
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\usage{
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xgb.importance(feature_names = NULL, model = NULL, trees = NULL,
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data = NULL, label = NULL, target = NULL)
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}
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\arguments{
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\item{feature_names}{character vector of feature names. If the model already
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contains feature names, those would be used when \code{feature_names=NULL} (default value).
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Non-null \code{feature_names} could be provided to override those in the model.}
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\item{model}{object of class \code{xgb.Booster}.}
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\item{trees}{(only for the gbtree booster) an integer vector of tree indices that should be included
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into the importance calculation. If set to \code{NULL}, all trees of the model are parsed.
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It could be useful, e.g., in multiclass classification to get feature importances
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for each class separately. IMPORTANT: the tree index in xgboost models
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is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).}
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\item{data}{deprecated.}
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\item{label}{deprecated.}
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\item{target}{deprecated.}
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}
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\value{
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For a tree model, a \code{data.table} with the following columns:
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\itemize{
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\item \code{Features} names of the features used in the model;
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\item \code{Gain} represents fractional contribution of each feature to the model based on
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the total gain of this feature's splits. Higher percentage means a more important
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predictive feature.
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\item \code{Cover} metric of the number of observation related to this feature;
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\item \code{Frequency} percentage representing the relative number of times
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a feature have been used in trees.
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}
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A linear model's importance \code{data.table} has the following columns:
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\itemize{
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\item \code{Features} names of the features used in the model;
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\item \code{Weight} the linear coefficient of this feature;
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\item \code{Class} (only for multiclass models) class label.
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}
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If \code{feature_names} is not provided and \code{model} doesn't have \code{feature_names},
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index of the features will be used instead. Because the index is extracted from the model dump
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(based on C++ code), it starts at 0 (as in C/C++ or Python) instead of 1 (usual in R).
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}
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\description{
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Creates a \code{data.table} of feature importances in a model.
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}
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\details{
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This function works for both linear and tree models.
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For linear models, the importance is the absolute magnitude of linear coefficients.
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For that reason, in order to obtain a meaningful ranking by importance for a linear model,
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the features need to be on the same scale (which you also would want to do when using either
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L1 or L2 regularization).
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}
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\examples{
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# binomial classification using gbtree:
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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 = 2,
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eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
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xgb.importance(model = bst)
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# binomial classification using gblinear:
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bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, booster = "gblinear",
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eta = 0.3, nthread = 1, nrounds = 20, objective = "binary:logistic")
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xgb.importance(model = bst)
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# multiclass classification using gbtree:
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nclass <- 3
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nrounds <- 10
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mbst <- xgboost(data = as.matrix(iris[, -5]), label = as.numeric(iris$Species) - 1,
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max_depth = 3, eta = 0.2, nthread = 2, nrounds = nrounds,
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objective = "multi:softprob", num_class = nclass)
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# all classes clumped together:
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xgb.importance(model = mbst)
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# inspect importances separately for each class:
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xgb.importance(model = mbst, trees = seq(from=0, by=nclass, length.out=nrounds))
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xgb.importance(model = mbst, trees = seq(from=1, by=nclass, length.out=nrounds))
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xgb.importance(model = mbst, trees = seq(from=2, by=nclass, length.out=nrounds))
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# multiclass classification using gblinear:
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mbst <- xgboost(data = scale(as.matrix(iris[, -5])), label = as.numeric(iris$Species) - 1,
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booster = "gblinear", eta = 0.2, nthread = 1, nrounds = 15,
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objective = "multi:softprob", num_class = nclass)
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xgb.importance(model = mbst)
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}
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