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Questions on Automodel (AM)
1. How does "weights" (given under "General" tab) differ from "feature sets". For example, in one simulation, AM shows that a certain input has an importance of 1, however by examining feature sets in a couple of algorithms (say 4 out 7) that were selected by AM for this analysis, these 4 algorithms do not select this particular input (when I view "feature sets").
2. In "Optimal trade-offs between complexity and error" graph. I can find a model of complexity of 4 and an error of 15%. However, for this particular algorithm the accuracy was 72%. I guess I am not sure on how these two relate to each other.
3. Given the above, what would be the best way to know the critical inputs in a dataset? Say that I trying to identify critical inputs in one dataset using AM and this is my thought process: what are these critical inputs for GLM, LR, DL, DT, RF, GBT etc. such that I can pinpoint identified inputs that re-occur between algorithms. I guess, this is my way of identifying such parameters (i.e. if they show up in different algorithms, then they are of high importance to the dataset). Any tips on this are appreciated. Thanks!