title: Sci-kit Learn Interdependent Classifiers
body: <p>I was wondering if anyone could provide some general guidance on this topic.</p>
  
  <p>I have a multi-class classification problem with many classes (like 400) but the classes are broken up into different columns (3 3 columns to be exact). They are also taxonomy based (2 depends on 1, 3 depends on 2). I'm trying to sort out the best way to handle stacking 3 models on top of each other to classify a record based on a document term matrix derived from a single text column (about 30k features).</p>
  
  <p>So the idea is to have classifier 1 classify the level 1 category and feed that same DTM to classifier 2 but only have level 2 categories available which are subsets of the level 1 category that classifier 1 chose and so on. This way there won't be any illogical predictions from level to level.</p>
  
  <p>Do you know if sci-kit learn has a good way to perform this kind of interdependent modeling? If not, what's the best way to do this in Pandas? I suppose I could filter the prediction lists past level 1 based on the previous predicted value and then take the argmax, but not sure if this will scale.</p>