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The most important thing missing in documentation #57

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vzemlys opened this issue May 26, 2017 · 9 comments
Closed

The most important thing missing in documentation #57

vzemlys opened this issue May 26, 2017 · 9 comments

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@vzemlys
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vzemlys commented May 26, 2017

So I have the recipe. What should I do next? How to pass the recipe to lm for example?

@treysp
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treysp commented May 26, 2017

You process() it to return a tibble containing the transformed data, then pass that to your estimation function (e.g., lm()), as described in Basic Recipes. See also the process() reference.

@topepo
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topepo commented May 26, 2017

So I have the recipe. What should I do next?

@treysp is correct but it is true that pushing the data that comes out of the recipe back into a formula is a bit underwhelming.

I'm in the process of making a recipe method for train and that should help a bit.

@vzemlys
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vzemlys commented May 26, 2017

Cool. It is not clear that process is the last step. Especially since it is in documentation part called Preprocessing. Also learn is I think a bit of a misnomer, since correct me if I am wrong learning is the part where you apply the model, not the part where you preprocess data.

Sorry for nitpicking. Hope the feedback is useful.

@topepo
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topepo commented May 26, 2017

Sorry for nitpicking. Hope the feedback is useful.

Absolutely!

It is not clear that process is the last step. Especially since it is in documentation part called Preprocessing

We're low on verbs to use 😬 . apply made more sense but it isn't a generic method.

I would say that process is the last step prior to model fitting (hopefully). The "pre-" is really pre-model fitting.

learn is I think a bit of a misnomer, since correct me if I am wrong learning is the part where you apply the model, not the part where you preprocess data.

The idea is that preprocessing of the data can be part of the overall modeling process and some of the steps involve estimating parameters in was that are similar to a classical model. For example, step_other needs to estimate the frequencies of class levels and so on. train was already taken by caret and some other packages.

The terminology is more similar to the machine learning communities where they would need to estimate/train/learn a model such as an autoencoder prior to fitting a neural network.

@treysp
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treysp commented May 26, 2017

Maybe using verbs that imply an ordering would clarify the process for people outside machine learning communities (like me).

Although one could take it too far, what about leveraging the recipe metaphor? Maybe something like:

  • First: create recipe object with recipe()
  • Second: add steps to your recipe with step_*()
  • Third: calculate intermediate elements such as frequencies of class levels with prep() (as in prepping ingredients)
  • Fourth step = execute the recipe with cook() or something similar

Not sure how hokey that sounds 😁

@topepo
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topepo commented May 26, 2017

prep is a pretty good word. Originally, I had thought of using cook or serve but we decided that we were stretching the food analogy at bit too far.

@treysp
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treysp commented May 30, 2017

What about make for the last step? It doesn't seem as contrived as cook, and I doubt there would be any confusion with GNU Make.

@hadley
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hadley commented May 30, 2017

I kind of like bake(). It rhymes with make() and is a bit more recipe like.

topepo added a commit that referenced this issue May 31, 2017
topepo added a commit that referenced this issue May 31, 2017
@topepo topepo closed this as completed Jun 8, 2017
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This issue has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex https://reprex.tidyverse.org) and link to this issue.

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