Unexpected results from Automatic Feature Engineering

User: "pblack476"
New Altair Community Member
Updated by Jocelyn
So I am trying to squeeze out the most accurate regression possible on my model, and for that I have narrowed GLM, GBT and SVM as the best learners for my data. I first try to optimize GLM as it trains the fastest.

I then generated a bunch of features with loops (manually) and selected the best broad group (this was still 400+ features we are talking about) for GLM. This group was not optimal for SVM or GBT but I wasn't optimizing that yet.

I then proceeded to run AFE on that Set to get the best GLM performance possible. It was no surprise that I got 8 or 9 optimal features that gave me the same GLM performance I had with 400+. So I was happy about that and applied that FeatureSet to my data so I would cut out the long AFE process.

However, this new dataset has considerably better performances in most learners. Including SVM and GBT. Even thou it was GLM optimized.

I then proceed to try and repeat the process for SVM, thinking that if I got such an improvement from a GLM oriented FeatureSet, I would get a better one from running AFE on SVM. But no. The SVM AFE returned a SIMPLER FeatureSet (even when I selected for Accuracy) with decent performance, but it did not beat the GLM AFE FeatureSet.

I did not think that was possible under most circumstances, but yet it happened.

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