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model-selection

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yellowbrick
bbengfort
bbengfort commented Aug 28, 2019

After the visualizer audit we believe that the quick methods are ready to be made prime time.

Quick methods must:

  1. Have a function signature identical to the Visualizer constructor with additional data for fit/score
  2. Call finalize() and not poof()/show()
  3. Return the fitted visualizer instance
  4. Some visualizers must also insure that they call transform/fit_transform/score
seanlaw
seanlaw commented Nov 11, 2019

In the tutorial, one of the URLs is pointing to the wrong location:

I Scholtes, N Wider, R Pfitzner, A Garas, CJ Tessone, F Schweitzer: Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks, Nature Communications, 5, September 2014

nhejazi
nhejazi commented Mar 19, 2018

In order to help with user migration, it would be really nice to have a "translation guide" for experienced users of the SuperLearner package to use sl3. This would take the place of a "SuperLearner3()" style wrapper function, instead helping users to find the correct sl3 idiom(s) to use in place of a given SuperLearner function or argument. An example of such would be the following:

A-gata
A-gata commented Jun 20, 2019

A clear and concise description of what you would like to see added to BAS.
I think the bestmodel argument is not clearly explained if one wants to modify it, would it be possible to include an example of using it with something other than the default?

I understand it as being able to specify which model should come first but it's not clear how and I haven't been able to determine that.

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