Calculation of variable importance for regression and classification models

A generic method for calculating variable importance for objects produced by train and method specific methods

## S3 method for class 'train':
varImp(object, useModel = TRUE, nonpara = TRUE, scale = TRUE, ...)

## S3 method for class 'earth': varImp(object, value = "gcv", ...)

## S3 method for class 'fda': varImp(object, value = "gcv", ...)

## S3 method for class 'rpart': varImp(object, surrogates = FALSE, competes = TRUE, ...)

## S3 method for class 'randomForest': varImp(object, ...)

## S3 method for class 'gbm': varImp(object, numTrees, ...)

## S3 method for class 'classbagg': varImp(object, ...)

## S3 method for class 'regbagg': varImp(object, ...)

## S3 method for class 'pamrtrained': varImp(object, threshold, data, ...)

## S3 method for class 'lm': varImp(object, ...)

## S3 method for class 'mvr': varImp(object, estimate = NULL, ...)

## S3 method for class 'bagEarth': varImp(object, ...)

## S3 method for class 'RandomForest': varImp(object, ...)

## S3 method for class 'rfe': varImp(object, drop = FALSE, ...)

## S3 method for class 'dsa': varImp(object, cuts = NULL, ...)

## S3 method for class 'multinom': varImp(object, ...)

## S3 method for class 'gam': varImp(object, ...)

## S3 method for class 'cubist': varImp(object, weights = c(0.5, 0.5), ...)

## S3 method for class 'JRip': varImp(object, ...)

## S3 method for class 'PART': varImp(object, ...)

## S3 method for class 'C5.0': varImp(object, ...)

## S3 method for class 'nnet': varImp(object, ...)

## S3 method for class 'glmnet': varImp(object, lambda = NULL, ...)

an object corresponding to a fitted model
use a model based technique for measuring variable importance? This is only used for some models (lm, pls, rf, rpart, gbm, pam and mars)
should nonparametric methods be used to assess the relationship between the features and response (only used with useModel = FALSE and only passed to filterVarImp).
should the importance values be scaled to 0 and 100?
parameters to pass to the specific varImp methods
the number of iterations (trees) to use in a boosted tree model
the shrinkage threshold (pamr models only)
the training set predictors (pamr models only)
the statistic that will be used to calculate importance: either gcv, nsubsets, or rss
should surrogate splits contribute to the importance calculation?
should competing splits contribute to the importance calculation?
which estimate of performance should be used? See mvrVal
a logical: should variables not included in the final set be calculated?
the number of rule sets to use in the model (for partDSA only)
a numeric vector of length two that weighs the usage of variabels in the rule conditions and the usuage in the linear models (see details below).
a single value of the penalty parameter

For models that do not have corresponding varImp methods, see filerVarImp.


Linear Models: the absolute value of the t--statistic for each model parameter is used.

Random Forest: varImp.randomForest and varImp.RandomForest are wrappers around the importance functions from the randomForest and party packages, respectively. Partial Least Squares: the variable importance measure here is based on weighted sums of the absolute regression coefficients. The weights are a function of the reduction of the sums of squares across the number of PLS components and are computed separately for each outcome. Therefore, the contribution of the coefficients are weighted proportionally to the reduction in the sums of squares. Recursive Partitioning: The reduction in the loss function (e.g. mean squared error) attributed to each variable at each split is tabulated and the sum is returned. Also, since there may be candidate variables that are important but are not used in a split, the top competing variables are also tabulated at each split. This can be turned off using the maxcompete argument in rpart.control. This method does not currently provide class--specific measures of importance when the response is a factor. Bagged Trees: The same methodology as a single tree is applied to all bootstrapped trees and the total importance is returned

Boosted Trees: varImp.gbm is a wrapper around the function from that package (see the gbm package vignette) Multivariate Adaptive Regression Splines: MARS models include a backwards elimination feature selection routine that looks at reductions in the generalized cross-validation (GCV) estimate of error. The varImp function tracks the changes in model statistics, such as the GCV, for each predictor and accumulates the reduction in the statistic when each predictor's feature is added to the model. This total reduction is used as the variable importance measure. If a predictor was never used in any of the MARS basis functions in the final model (after pruning), it has an importance value of zero. Prior to June 2008, the package used an internal function for these calculations. Currently, the varImp is a wrapper to the evimp function in the earth package. There are three statistics that can be used to estimate variable importance in MARS models. Using varImp(object, value = "gcv") tracks the reduction in the generalized cross-validation statistic as terms are added. However, there are some cases when terms are retained in the model that result in an increase in GCV. Negative variable importance values for MARS are set to zero. Alternatively, using varImp(object, value = "rss") monitors the change in the residual sums of squares (RSS) as terms are added, which will never be negative. Also, the option varImp(object, value ="nsubsets"), which counts the number of subsets where the variable is used (in the final, pruned model). Nearest shrunken centroids: The difference between the class centroids and the overall centroid is used to measure the variable influence (see pamr.predict). The larger the difference between the class centroid and the overall center of the data, the larger the separation between the classes. The training set predictions must be supplied when an object of class pamrtrained is given to varImp.

Cubist: The Cubist output contains variable usage statistics. It gives the percentage of times where each variable was used in a condition and/or a linear model. Note that this output will probably be inconsistent with the rules shown in the output from summary.cubist. At each split of the tree, Cubist saves a linear model (after feature selection) that is allowed to have terms for each variable used in the current split or any split above it. Quinlan (1992) discusses a smoothing algorithm where each model prediction is a linear combination of the parent and child model along the tree. As such, the final prediction is a function of all the linear models from the initial node to the terminal node. The percentages shown in the Cubist output reflects all the models involved in prediction (as opposed to the terminal models shown in the output). The variable importance used here is a linear combination of the usage in the rule conditions and the model. PART and JRip: For these rule-based models, the importance for a predictor is simply the number of rules that involve the predictor.

C5.0: C5.0 measures predictor importance by determining the percentage of training set samples that fall into all the terminal nodes after the split. For example, the predictor in the first split automatically has an importance measurement of 100 percent since all samples are affected by this split. Other predictors may be used frequently in splits, but if the terminal nodes cover only a handful of training set samples, the importance scores may be close to zero. The same strategy is applied to rule-based models and boosted versions of the model. The underlying function can also return the number of times each predictor was involved in a split by using the option metric = "usage".

Neural Networks: The method used here is based on Gevrey et al (2003), which uses combinations of the absolute values of the weights. For classification models, the class-specific importances will be the same.


  • A data frame with class c("varImp.train", "data.frame") for varImp.train or a matrix for other models.


Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3), 249-264.

Quinlan, J. (1992). Learning with continuous classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence, 343-348.

  • varImp
  • varImp.train
  • varImp.rpart
  • varImp.randomForest
  • varImp.gbm
  • varImp.regbagg
  • varImp.classbagg
  • varImp.pamrtrained
  • varImp.lm
  • varImp.mvr
  • varImp.bagEarth
  • varImp.RandomForest
  • varImp.rfe
  • varImp.dsa
  • varImp.fda
  • varImp.multinom
  • varImp.gam
  • varImp.cubist
  • varImp.JRip
  • varImp.PART
  • varImp.nnet
  • varImp.C5.0
  • varImp.glmnet
Documentation reproduced from package caret, version 5.15-045, License: GPL-2

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