Calculate the Median of the results from 100 Simulations

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CMatlabWold
CMatlabWold 2021-10-13
评论： CMatlabWold 2021-10-14
Hi. I have a code where I am running a Random Forest regression. I am running it 100 times. However, I am having difficulty calculating the median of the 100 trials.
The result I am looking for is located in the variable designated "impOOB".
For each run, there should be values in impOOB variable for 5 columns. For instance:
0.427417559041683 0.00894308188405568 0.141297948087486 0.222153283589539 0.200188127397237
For 100 runs of column 1, I need the median. The same for column 2, and so forth.
My code is as follows:
n = 100;
result = zeros(n,5);
for k=1:n
X = readtable('TOPOonly.xlsx','PreserveVariableNames',true)
Y = readtable('TotalComplaintsRF.xlsx','PreserveVariableNames',true)
t = templateTree('NumVariablesToSample','all',...
'PredictorSelection','interaction-curvature','Surrogate','on');
Mdl = fitrensemble(X,Y,'Method','Bag','NumLearningCycles',200, ...
'Learners',t);
yHat = oobPredict(Mdl);
R2 = corr(Mdl.Y,yHat)^2
impOOB = oobPermutedPredictorImportance(Mdl);
impOOB(impOOB<0) = 0;
impOOB = impOOB./sum(impOOB)
result(k) =
end
I'll attach the files as well. I appreciate very much any help with this.

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Matt J
Matt J 2021-10-14
编辑：Matt J 2021-10-14
Ran in:
impOOB=rand(100,5)
impOOB = 100×5
0.7604 0.5152 0.7196 0.2418 0.5420 0.8386 0.3787 0.4701 0.4692 0.7572 0.9929 0.0561 0.2087 0.1176 0.9434 0.7796 0.2491 0.1337 0.1499 0.9048 0.3695 0.1500 0.6826 0.4575 0.4751 0.6069 0.1207 0.8111 0.5832 0.3273 0.9885 0.2647 0.1840 0.9606 0.0610 0.1243 0.3249 0.5171 0.1649 0.9400 0.7085 0.7869 0.5282 0.5472 0.4634 0.7656 0.4034 0.7932 0.8618 0.5136
median(impOOB,1)
ans = 1×5
0.4813 0.4997 0.4696 0.4513 0.5518
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Matt J
Matt J 2021-10-14
n = 100;
result = zeros(n,5);
for k=1:n
X = readtable('TOPOonly.xlsx','PreserveVariableNames',true)
Y = readtable('TotalComplaintsRF.xlsx','PreserveVariableNames',true)
t = templateTree('NumVariablesToSample','all',...
'PredictorSelection','interaction-curvature','Surrogate','on');
Mdl = fitrensemble(X,Y,'Method','Bag','NumLearningCycles',200, ...
'Learners',t);
yHat = oobPredict(Mdl);
R2 = corr(Mdl.Y,yHat)^2
impOOB = oobPermutedPredictorImportance(Mdl);
impOOB(impOOB<0) = 0;
result(k,:) = impOOB./sum(impOOB);
end
median(result,1)

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