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[D] what's the foundations of data modeling? (alien.top)

submitted 1 year ago by Stack3@alien.top to c/machinelearning@academy.garden

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I came up with this thought experiment today because I'm trying to get at the heart of how to approximate a function. TDLR: if you know the foundational principles of that, it's really my whole question.

I thought, ok, you are given a deterministic dataset and asked to model it perfectly. Perfectly means you extract every last ounce of information out of it, you can predict the dataset with 100% accuracy and you will be given new observations to predict that are more of the same so you should be able to predict those too.

You are given a magic computer to make this model with. It's infinitely fast and has infinite memory. So you have no constraints, no limitations. You can do anything, but you must do it. You must write a way to build a perfect model. You can brute force it, but it has to learn the perfect model.

What do you do? What does the simplest algorithm to perfectly model the data look like?

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[–] Paluth@alien.top 1 points 1 year ago

If one had available a computer with infinite memory and instant compute, then there would be no need to create a Perfect Model. Lets call this theoretical computer PC (Perfect Computer). With a PC available to the developer the goal changes from creating a perfect model, to creating a model that can create, train, and test other models for the problem. This model would than create infinite models, train and test than all. Select the best one, than replace itself with and best one. Then the new model repeats this process again. And again. To Infinity. Since our PC can compute all this instantly, we would instantly have a perfect model, and that model is the infinitesimal winner of the contest between the infinite designs that were created by the preview's generation winner.