MATLAB: Map of Trees For Classification Models

classificationmachine learningmap

For the models created using the Classification Learner App, is it possible to see a map of the decision trees and how many samples are split to each branch?

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Hi John,

You can view the decision tree using the Classification tree viewer. Here is the link containing more information regarding this: https://www.mathworks.com/help/stats/fitctree.html

Thanks,

Swarnava Pramanik

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MATLAB: Can someone tell me about the algorithm used to obtain the prediction of the bagged models in Ensemble bagged tree classification in Matlab

In general ensemble classifiers combine a different variety of weak classifiers to reduce high variance. So in general the weak leraners could be any classification models, from logistic regression to nueral nets and could be using any optimization techniques to reduce loss. But in an Ensemble Bagged tree ( I think you are talking about this https://www.mathworks.com/help/stats/treebagger-class.html ) classifier the models would be weak decision trees in which a bootstrap data is drawn for each tree. Decision trees ususally use gini index or entrophy to decide their next branch. Adaboost can also be applied to any ensemble classifier. So in an Ensemble Bagged tree classification all models would be decision trees with varying data due to bagging.

MATLAB: Classification using decision tree

I am assuming that there may be some problem with the file names as the file ‘ECGFiveDays_TRAIN.xlsx’ contains only 23 records and ‘ECGFiveDays_TEST.xlsx’ contains 861 records. It may not be optimal to fit the decision tree using just 23 records and then evaluate the resulting model on a bigger dataset.

So, for the following code I have taken the liberty of using the bigger dataset as the training data. Please make use of the following code snippet to perform the classification using decision trees.

clear; 
trainData = xlsread('ECGFiveDays_TEST.xlsx'); 
testData = xlsread('ECGFiveDays_TRAIN.xlsx'); 
tree = fitctree(trainData(:,2:end),trainData(:,1));        %Fit the dataset using decision tree 
predictLabels = predict(tree,testData(:,2:end));            %Evaluate on test dataset 
trueLabels = testData(:,1); 
testAccuracy = sum(predictLabels == trueLabels)/length(trueLabels);

Please go through the following documentation link on “fitctree()” if you need any further clarifications on the same

https://www.mathworks.com/help/stats/fitctree.html

Best Answer

Related Solutions

MATLAB: Can someone tell me about the algorithm used to obtain the prediction of the bagged models in Ensemble bagged tree classification in Matlab

MATLAB: Classification using decision tree

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