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POUsing OpenCV Random Forests, is there any way to obtain the "confidence" level for a classification?
 

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  1. POUsing OpenCV Random Forests, is there any way to obtain the "confidence" level for a classification?
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    <p>If I have a trained random forest, is there any way for me to obtain the number of votes that each class got by the forest on a test sample? A percentage of votes would be even better.</p> <p>Something like CVRTrees::predict, but getting raw output along with the predicted class.</p> <p>Thanks</p> <p><strong>Edit</strong> To further explain my goal, so that I could potentially get an answer that solves my problem and not necessarily my question.</p> <p>To answer how much I know, it is very little. </p> <p>This is a real world application, and I am trying to get myself up to speed as quickly as possible on all of this. </p> <p>Essentially, I researching discriminative classifier, with a requirement that I need to be able to compare the output between 2 (or more) independent classifiers. I mean independent in the sense that they may or may not know about the entire set of classes, however there does exist a set of classes where all classifiers contain a subset of such classes. </p> <p>My initial though is to collect meta-information about the classification from each of the classifiers, which ideally would contain some form of (there is a 15% of it being A, and 78% chance of being B) [I know chance is a bad word, but I will leave it]. If I could get that output, I would be able to perform a final classification based on dynamic performance weights assigned to each classifier. </p> <p>The idea is that I can use a very simple rule based classifier to do initial classifications while the more exotic classifier has time to train. Ideally, the learning classifier could potentially support more classes than the rule classifier and over time it is primarily used.</p>
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    1. COHave you seen [randomforest.cpp](http://public.cranfield.ac.uk/c5354/teaching/ml/examples/c++/opticaldigits_ex/randomforest.cpp)?
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    2. CO@karlphillip Yes, I have seen it. It doesn't show any example of getting the meta information from the classification like I am looking for. I am just as interested in _why_ the result was a class as I am in the actual result. If random forests is not suitable for this information, I am open to other learning structures that do provide that information.
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    3. CO1. RF is very easy to implement your own. Also as there is a high degree of freedom practically, e.g. tests strength, randomness, you would have a better grip on what is done if you do it yourself. 2. Can you actually interpret more if you have the distributions rather than only accuracies?
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