Force classifer to select a fixed number of targets
Just do it yourself. Each classifier in scikit-learn gives you access to decision_function or predict_proba, both of which are support for predict operation (predict is just argmax of these). Thus - just select 100 with the highest support.
If I have a dataset with events where each event has data with 1000 possible items with only 100 being correct for each event. How do I force my classifier to select only 100 for each event?
After I run it through my training model (with 18 features and always has 100 targets/event flagged as 1) the classifier selects anywhere between 60-80 items instead of 100. Even if I give each event an event number that doesn't help.
I'm using python sklearn gradient boosting and random forest method.