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