Random forest prediction probabilities

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Memo Remo
Memo Remo 2021-4-13
评论： Memo Remo 2021-6-22
Hi,
I trained a random forest model using MATLAB's "TreeBagger" function. However, when I use the "predict" function, my probabilities are all 0 or 1 except for a few predictions. Despite having 4000 observations, my roc curve has also only three data point. Can you suggest any solution for this problem?
Thanks in advance.
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Memo Remo
Memo Remo 2021-4-13
编辑：Memo Remo 2021-4-13
Thanks for the reply,
Attached is the data and and my code is copied below:
**************************************************
rng default
Y=TRAIN(:,7);
X_select=[1,2,3,6];
X=[TRAIN(:,X_select)];
CVO = cvpartition(Y,'k',5);
for i = 1:CVO.NumTestSets
i
clear PredictedLabels PredictedProbabilities PredictedProbabilities_Cell Y_test X_test teIdx
clear PredictedLabels4Tree TreeProb PredictedScores4Tree SelectedTree SelectedTreeID H idxvar
clear Y_train X_train trIdx
trIdx = CVO.training(i);
X_train=X(trIdx,:);
Y_train=Y(trIdx,:);
b = TreeBagger(50,X_train,Y_train,'oobvarimp','on');
idxvar = find(b.OOBPermutedVarDeltaError>0.75)
b5v = TreeBagger(100,X_train(:,idxvar),Y_train,'oobpred','on','OOBPredictorImportance','on');
H=diff(oobError(b5v));
SelectedTreeID=find(abs(H)<0.001);
if(isempty(SelectedTreeID)==1)
error('Increase the number of grown trees!')
end
SelectedTree=b5v.Trees{SelectedTreeID(1)};
[PredictedProbabilities4Tree PredictedScores4Tree]=predict(SelectedTree,X_train(:,idxvar));
TreeProb=cell2mat(PredictedProbabilities4Tree);
for r=1:size(PredictedProbabilities4Tree,1)
PredictedLabels4Tree(r)=round(str2num(TreeProb(r)));
end
mdl_RF{i}=SelectedTree;
[fprRF,tprRF,~,AUC_RF] = perfcurve(Y_train,PredictedScores4Tree(:,2),'1');
teIdx = CVO.test(i);
X_test=X(teIdx,:);
Y_test=Y(teIdx,:);
PredictedProbabilities_Cell=(predict(mdl_RF{i},X_test));
for m=1:length(PredictedProbabilities_Cell)
PredictedProbabilities(m,1)=str2num(PredictedProbabilities_Cell{m});
end
PredictedLabels=round(PredictedProbabilities);
[X_roc_RF{i},Y_roc_RF{i},T_roc_RF{i},AUCs_RF(j)] = perfcurve(Y_test,PredictedProbabilities,'1');
end
figure
plot(X_roc_RF{4},Y_roc_RF{4})

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采纳的回答

Aditya Patil
Aditya Patil 2021-5-10
Getting a probability of 1 suggests that the model has overfitted, and the observation is being predicted as belonging to the specific class by all trees.
You can overcome this issue by reducing the size of the trees. Few of the options that might help are,
  1. MinLeafSize: Set this to higher value
  2. MaxNumSplits: Set this to a lower value
You can also try to use fitcensemble instead. See TreeBagger and fitcensemble for more details.
Alternately, you may want to use a different approach entirely, by using SVMs or other classifiers.

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