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Well, there are some statistical learning models that are explainable. Decision trees can be induced and can "explain themselves", just like in expert systems.
by colllectorof 7y ago
Well, there are some statistical learning models that are explainable. Decision trees can be induced and can "explain themselves", just like in expert systems. Additive models are self-explanatory.
Not sure I'm sold on LIME and other similar approaches, though. Seems like a lot of deep learning people are all too happy to substitute "intepretability" for actual explanations.
- YeGoblynQueenne 7y agoDecision trees are not a statistical model. The learning method is statistical, if you will, but the model itself is a disjunction of conjunctions- a symbolic model (and more specifically, a propositional logic theory). Decision Trees are in fact an example of the early years of machine learning where the trend was towards algorithms and techniques that learned symbolic theories. I believe the effort was driven by the realisation that expert systems had a certain problem with knowlege acquisition [1] which drove people to try and learn production rules from data. I digress- I mean to say that decision trees are explainable because their models are not statistical. To be honest, I don't know much about additive models. _______________ [1] https://en.wikipedia.org/wiki/Knowledge_acquisition https://en.wikipedia.org/wiki/Knowledge_acquisition