Assign to a 'protected' property of TreeBagger object

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Ian
Ian on 21 Mar 2014
Commented: Ian on 21 Mar 2014
Not an expert on MATLAB OO programing here, and I have a seemingly simple problem. I want to change a property value that is 'protected' on a built-in class (not a class I developed or even know my way around), but there's no obvious method to do so. So how do I change it?
Example time ... here's the property I'm looking at:
load fisheriris
B = TreeBagger(10, meas, species);
T = B.Trees{1};
T.ClassProb
I need to twiddle with the values in that matrix, without breaking the predict method for TreeBagger.

Answers (2)

Sean de Wolski
Sean de Wolski on 21 Mar 2014
The property is protected to make sure that you can't interfere with the operation of the TreeBagger object. If you need access to it, I suggest contacting us and providing us with a use-case that we could then consider for an enhancement.
Okay, so how do you actually do this? Write your own class that inherits from TreeBagger. Since TreeBagger will be a superclass of your class, your class will be able to overwrite the protected properties.
  3 Comments
Ian
Ian on 21 Mar 2014
Why? Nothing deeply troubling. I just have a different method for calculating scores associated with the random forest predictions. I can implement it one of two ways, by looping through all the trees and eval each one individually or by modifying ClassProb and using the (presumably speedier) predict method. I have a lot of predictions to make.

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Ilya
Ilya on 21 Mar 2014
I suggest that you leave ClassProb alone. If you need a different method for prediction, loop through the trees. The likelihood of making a coding error in this approach would be much lower than that of making a coding error in the derived class.
bagger.Trees{1} is an instance of class CompactClassificationTree. So you would need to derive a new tree class from that class and let the new class set values of the ClassProb property. Then you would need to derive a new class from TreeBagger and make this new class use the new tree class instead of CompactClassificationTree. And even then it wouldn't work because ClassProb is a dependent property, and you need to change its value in the implementation class, which is one more layer down, to affect prediction.
  1 Comment
Ian
Ian on 21 Mar 2014
Thanks for spelling out all the levels of the problem.

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