[hamhamed]

We ourselves use BigML and AutoML from google, and we have engineers in our team who can do TF. It's just less of a pain in the ass, and the models are very reliable.

I'll give this a try, though I'm not sure what strategy it's using (I'm assuming an ensemble?)

Also you're asking the user if the objective should be classification or regression, I think that should be automatic, it's already buzzwords for your typical marketing person :)

GJ and gl!!

[halflings]

BigML and AutoML are great! And I myself write a lot of custom TF code.

I see this as addressing a different stage (rapid/cheap prototyping) and audience (less technical people, smaller enterprises); it'll never be as powerful & state-of-the-art as advanced ML tools by cloud providers, and that's OK :)

> I'll give this a try, though I'm not sure what strategy it's using (I'm assuming an ensemble?)

It's embarrassingly simple right now: a fully-connected DNN with a static architecture (hand-picked because it worked fine for all datasets I tried), but you can also enable ensembling and/or change the network's architecture (just click on "Show advanced settings")

> Also you're asking the user if the objective should be classification or regression, I think that should be automatic, it's already buzzwords for your typical marketing person :)

Good point, but it's not always something that can be determined automatically. I did add some heuristics to warn people when we think they are wrong (e.g. running classification on a numeric variable with too many unique values, usually the sign it should be a regression problem instead).

One thing that might help here is that the redesigned flow shows a step-by-step instructions where one step is to pick the task (regression vs classification), and we'll add some text there to help people understand what the difference is. I'll also consider the heuristic approach going forward! (and make sure it doesn't override cases where users already manually set the task)