Force the random forest classifier to use all the input features?
What it can help you is setting the parameter max_feature = 1, so each node will take a (uniform distributed) random feature, and it will be forced to use it. Nevertheless, you need to set the depth of the tree too, because it will be infinitely adding nods till receiving one of the main features.
To perform a binary prediction, I have 5 features which I want to use for my random forest classifier and two are them are not being utilized at all. I understand that this is the whole point of Machine Learning to select the useful features only, but the other three features might have biased data and I want to make sure that all my features are being used at equal weightage to run my classifier. I can't find a straight forward answer to this question. I use sklearn in python for this work. Any comments/suggestions would be greatly appreciated.