Need Predictor Importance in Random Forest Expressed as a Percentage

Hi. I'm running a code to see the importance of demographics (Predictors) on my response (Complaints). I need to express the importance as percentage, as a scale of 0 to 1 (or 0% to 100%). This is the figure I am getting is attached as "RF Importance Chart". My predictors data is attached as "PredictorsOnly.xlsx" and my response data is attached as "TotalComplaintsRF.xlsx"

X = readtable('PredictorsOnly.xlsx','PreserveVariableNames',true)
Y = readtable('TotalComplaintsRF.xlsx','PreserveVariableNames',true)

t = templateTree('NumVariablesToSample','all',...
    'PredictorSelection','interaction-curvature','Surrogate','on');
rng(1); % For reproducibility
Mdl = fitrensemble(X,Y,'Method','Bag','NumLearningCycles',200, ...
    'Learners',t);
yHat = oobPredict(Mdl);
R2 = corr(Mdl.Y,yHat)^2
impOOB = oobPermutedPredictorImportance(Mdl);
figure
bar(impOOB)
title('Unbiased Predictor Importance Estimates')
xlabel('Predictor variable')
ylabel('Importance')
h = gca;
h.XTickLabel = Mdl.PredictorNames;
h.XTickLabelRotation = 45;
h.TickLabelInterpreter = 'none';

Answer 1

[Pratyush Roy]

Hi,

oobPermutedPredictorImportance normalizes the predictor importance by the standard error (this is common practice in the field), therefore values are not strictly scaled between 0 and 1. However one can rescale predictor importance, for example:

imp(imp<0) = 0;
imp = imp./sum(imp);

Hope this helps!

[CMatlabWold]

Thank you - it works!