Predictor Importance for Bagged Trees in Classification Learner App??
Hello,
I have sucessfully used BaggedTrees in the classificationLearner app to classify my data. I would now like to know (and show) which of my predictors are most useful in determining class. How can I do this?
The scatter plots generated in the app are helpful, but not what I am looking for. I want something like a bar graph that shows the most and least relevant predictors, or ideally a decision tree which shows which predictors give the greatest class separations.
I have found examples of this with different set ups, but I can’t figure out how to make them work for me. The command I have seen to make decision trees is: view(trainedModel.ClassificationTree,’Mode’,’graph’)
I have also seen the predictor importance found using: imp = predictorImportance(ens)
However, these methods have not worked for me. My code is simply a data importation and the command to call the classifier: fit = trainedModel2.predictFcn(Table);
Any help would be HUGELY appreciated.
NOTE:-
Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.
Hi,
“I have sucessfully used BaggedTrees in the classificationLearner app to classify my data…”
1. Make sure to have exported the model (not the compact model) from the classification learner app. Then you find something like this by double click on your trained model in workspace. Important is that you have a ClassificationBaggedEnsemble:
2. If you have this model as a ClassificationBaggedEnsemble in your workspace, you can use:
SEE COMPLETE ANSWER CLICK THE LINK
