% 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, 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{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 only two columns:
\itemize{
  \item \code{Features} names of the features used in the model;
  \item \code{Weight} the linear coefficient of this feature.
}

If you don't provide or \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{

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)

}