You are saying something very different from what the usage note says. You are saying the model is trained on a small set of data, and that model is the one used. The usage note says the technique shortens the training time using the full data set. Does Variable Selection, set for two-way interactions, accomplish the same thing that would be accomplished with logistic regression being set to two-way? I have selected the relevant variables. A couple of the class inputs have a large number of levels (200 and 60), which I have greatly reduced just for the logistic regression models, but they still don't build. HP Trees, HP Forest, and Neural Networks all build very quickly, without reducing those levels. I understand that the logistic regression technique gets exponentially larger with class variables with a high number of levels, as opposed to the other techniques. That is why I was trying to "start closer" to the final parameters by implementing that usage note. Anyway, I have moved on. All of the other modeling techniques are simply beating logistic regression, the way I can run it. (I've already used the Variable Selection node in front of it, but I still have to greatly reduce the number of levels of those two inputs for it to complete, and setting two-way interactions in the Regression node is a no-go.)
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