automl-gs

AI

automl

SHOW HN2019

automl-gs

Provide a CSV and a target field, generate a model and code to run it

github.com

#automl#machine learning#hyperparameter optimization#csv#tensorflow

172points
26comments
#236of 2,195 in 2019
Top 11%of 2019 by points

How HN took it

MixedNo clear lean

3 positive, 4 neutral or asking, 0 mixed, 2 critical, out of 9 comments about the project. Thread score +0.09.

Idea: 2 for, 1 againstDocs: 1 for, 1 against

The thread, comment by comment

Each row is a top-level comment and its replies, in HN's order. Select a cell to open that comment on HN.

positive neutral or mixed critical about another commenter or something else the maker

What HN said

What people liked

  • Well explained and motivated project with a useful DESIGN.md

    From 1 comment
    • This is extremely cool/useful. Really appreciated how well the project is explained/motivated, e.g. the DESIGN.md file. Thanks for making it public! Question about the long-term…
      nickvincent on HN
  • Useful, cool project

    From 2 comments
    • This is fantastic! Thanks for putting this on Github. I am planning to build a [R] Shiny app for automl. This gives me good learning opportunity.
      malshe on HN
    • Cool project! Have you compared your results to Ludwig by Uber? It would be interesting to see where the results differ. https://uber.github.io/ludwig/
      ptd on HN

Objections

  • Gridsearch is inferior to random search for hyperparameter optimization

    From 1 comment
    • Nice project! You use gridsearch for hyperparamter optimization and state that at some point you would like to add a bayesian approach. One simple change that could boost the…
      human_scientist on HN
  • Use of the term 'native Python code' is confusing

    From 1 comment
    • Please, consider changing instances of "Generates native Python code" to something like "Generates standard Python code without 3rd party libraries dependencies" or equivalent.…
      jdright on HN
  • Early stopping is not implemented

    From 1 comment
    • Congratulations on lunching! I'm also working on open source autoML solution and I have similar problem with finding good heuristics for column type inference - I always left the…
      pplonski86 on HN

Requests

  • Compare accuracy against other AutoML frameworks

    From 2 comments
    • Congratulations on lunching! I'm also working on open source autoML solution and I have similar problem with finding good heuristics for column type inference - I always left the…
      pplonski86 on HN
    • Cool project! Have you compared your results to Ludwig by Uber? It would be interesting to see where the results differ. https://uber.github.io/ludwig/
      ptd on HN
  • Add early stopping

    From 2 comments
    • Congratulations on lunching! I'm also working on open source autoML solution and I have similar problem with finding good heuristics for column type inference - I always left the…
      pplonski86 on HN
    • > Have you compared the solution accuracy with other frameworks? (or is it not the priority for your package right now) Still working on that. > Do you have early stopping…
      minimaxir on HN
  • Longer demo gif and different demo dataset

    From 1 comment
    • The titanic data is a nice demo, but I’m kinda disappointed you didn’t try to predict WWMWD? Longer demo gif please!
      caseyf7 on HN

Questions

  • How are models architected?

    From 1 comment
    • Very cool. Can you provide some details about how this tool architects models?
      ashelmire on HN
  • How does it compare to Ludwig by Uber?

    From 1 comment
    • Cool project! Have you compared your results to Ludwig by Uber? It would be interesting to see where the results differ. https://uber.github.io/ludwig/
      ptd on HN

Alternatives named

  • Ludwig by Uber

    From 1 comment
    • Cool project! Have you compared your results to Ludwig by Uber? It would be interesting to see where the results differ. https://uber.github.io/ludwig/
      ptd on HN
  • HyperOpt

    From 1 comment

Category, keywords, summaries and labels on this page were made by AI (Thread Reader v2) and not checked by hand. Each point links to the comments it came from.

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