MATLAB: How to use the “predictorImportance” function with models generated by applying “crossval” to RegressionTree objects

crossvalfitrtreeimportancepredictorStatistics and Machine Learning Toolbox

When I apply the ‘crossval’ function on the results of the ‘fitrtree’ function, I end up with a different class, namely: 'classreg.learning.partition.RegressionPartitionedModel' and am unable to use the 'predictorImportance' function on this object. How do you call 'predictorImportance' on the models generated from 'crossval'?

Best Answer

It is important to note that ‘predictorImportance’ can only be applied to one model at a time. The result of using the “crossval” function on a regression tree will be a set of regression models and thus you will need to index into each of these models to determine the predictor importance.
The following example code which you can execute in the MATLAB command window shows how you can call ‘predictorImportance’ on the results of ‘crossval’
%%Load the sample data.
load carsmall; 
%%Construct a regression tree using the sample data.
tree = fitrtree([Weight, Cylinders],MPG,...
                'categoricalpredictors',2,'MinParentSize',20,...
                'PredictorNames',{'W','C'})  ;
%%cross validation
Ctree = crossval(tree);
% where tree is the original ‘RegressionTree’ object. Note that the default number of folds i.e. regression models generated will be 10
%%Calling predictorImportance on individual regression models
predictorImportance(Ctree.Trained{1})
predictorImportance(Ctree.Trained{10})
% where 1 and 10 represent the indices of the trained models. This would range from 1 to 10 by default
 
For more information on how to use the ‘fitrtree’ function please refer to the following link:
https://www.mathworks.com/help/stats/fitrtree.html
 
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