b4d97d3cb8
* [R] better argument check in xgb.DMatrix; fixes #1480 * [R] showsd was a dummy; fixes #2044 * [R] better categorical encoding explanation in vignette; fixes #1989 * [R] new roxygen version docs update
66 lines
2.4 KiB
R
66 lines
2.4 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, data = NULL,
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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{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 only two 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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}
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If you don't provide or \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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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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}
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