Context: I have some data to fit a random forest classifier (binary output) with 1 being a very rare event. In particular, in my training set, there are only 614 1's out of 29400 points. I am using sklearn RandomForestClassifier.
I am setting
class_weight = balanced to prevent the model from simply predicting 0 to every case. And it is working great!
However, there is also a small group within the 0 class (maybe only 20 - 30 cases) (Edit: actually about 300) that I want my model to capture. I believe because of the sampling natures when building trees, these class are not chosen very often. Is there a known way to solve this problem?
Add an additional filter after the RF. Trouble is, it's hard to find some easy categorization methods for these 20 -30 negative cases.
Force the RF to include these samples when building the trees. Hence this post...