[HN Gopher] Show HN: Web App with GUI for AutoML on Tabular Data
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       Show HN: Web App with GUI for AutoML on Tabular Data
        
       Author : pplonski86
       Score  : 35 points
       Date   : 2023-08-24 10:40 UTC (12 hours ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | pplonski86 wrote:
       | Web App is using two open-source packages that I've created:
       | 
       | - MLJAR AutoML - Python package for AutoML on tabular data
       | https://github.com/mljar/mljar-supervised
       | 
       | - Mercury - framework for converting Jupyter Notebooks into Web
       | App https://github.com/mljar/mercury
       | 
       | You can run Web App locally. What is more, you can adjust
       | notebook's code for your needs. For example, you can set
       | different validation strategies or evalutaion metrics or longer
       | training times. The notebooks in the repo are good starting point
       | for you to develop more advanced apps.
        
       | canvascritic wrote:
       | The real challenge with AutoML isn't just the pipeline creation,
       | but ensuring that the models generalize well to unseen data and
       | real-world scenarios. Your project here is neat and I appreciate
       | that it includes support for preprocessing and model explanations
       | as these are often overlooked.
       | 
       | I wonder though about the robustness and reliability of the
       | models it generates. automl, in its essence, risks overfitting or
       | underfitting if not carefully managed, and introduces a layer of
       | indirection that can make debugging far more difficult. how does
       | this project avoid those pitfalls?
       | 
       | It would be interesting to see how your tool performs in diverse
       | datasets and how resilient the models are against drift over
       | time. nevertheless, it's always good to see new takes on AutoML.
       | keep probing the space and refining your approach
        
         | pplonski86 wrote:
         | Here is benchmark done by independent team of researchers
         | https://openml.github.io/automlbenchmark/
         | 
         | I think most of overfitting is avoided with early stoppoing
         | technique.
         | 
         | The underfitting can be avoidwd with using large training time.
        
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       (page generated 2023-08-24 23:02 UTC)