predictor importance estimates by permutation of out-凯发k8网页登录
predictor importance estimates by permutation of out-of-bag predictor observations for random forest of classification trees
description
returns
a vector of out-of-bag,
predictor importance estimates by permutation using the random
forest of classification trees imp
= oobpermutedpredictorimportance(mdl
)mdl
. mdl
must
be a model
object.
uses
additional options specified by one or more imp
= oobpermutedpredictorimportance(mdl
,name,value
)name,value
pair
arguments. for example, you can speed up computation using parallel
computing or indicate which trees to use in the predictor importance
estimation.
input arguments
output arguments
examples
more about
tips
when growing a random forest using fitcensemble
:
standard cart tends to select split predictors containing many distinct values, e.g., continuous variables, over those containing few distinct values, e.g., categorical variables [3]. if the predictor data set is heterogeneous, or if there are predictors that have relatively fewer distinct values than other variables, then consider specifying the curvature or interaction test.
trees grown using standard cart are not sensitive to predictor variable interactions. also, such trees are less likely to identify important variables in the presence of many irrelevant predictors than the application of the interaction test. therefore, to account for predictor interactions and identify importance variables in the presence of many irrelevant variables, specify the interaction test [2].
if the training data includes many predictors and you want to analyze predictor importance, then specify of the
templatetree
function as'all'
for the tree learners of the ensemble. otherwise, the software might not select some predictors, underestimating their importance.
for more details, see and .
references
[1] breiman, l., j. friedman, r. olshen, and c. stone. classification and regression trees. boca raton, fl: crc press, 1984.
[2] loh, w.y. “regression trees with unbiased variable selection and interaction detection.” statistica sinica, vol. 12, 2002, pp. 361–386.
[3] loh, w.y. and y.s. shih. “split selection methods for classification trees.” statistica sinica, vol. 7, 1997, pp. 815–840.
extended capabilities
version history
introduced in r2016b