I am using the api to train a DecisionTree and then use it to classify. I was having issues the tree was not working properly at all. After doing some debugging and digging through the source I found that when an instance is compressed binary features always get assigned a weight of 1. If I don't compress the instances then the tree trains and classifies just as expected. Is this a bug in the compressed instances? If not how are weights and binary features supposed to be used?
Hmm, maybe you've found a bug!
Can you tell us where you believe the bug is located so we can quickly check to see what's wrong with it? Thanks :)
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