Does Neuton framework offer better ML algorithms than the ones currently known to the public?

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I came across an article about new proprietary 'Neuton' framework.



I can't find educated opinions from professionals in the field. What the framework promises feels 'too good to be true'. Is it?










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    up vote
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    down vote

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    I came across an article about new proprietary 'Neuton' framework.



    I can't find educated opinions from professionals in the field. What the framework promises feels 'too good to be true'. Is it?










    share|cite|improve this question









    New contributor




    neutonquestiononetimeuser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.





















      up vote
      1
      down vote

      favorite









      up vote
      1
      down vote

      favorite











      I came across an article about new proprietary 'Neuton' framework.



      I can't find educated opinions from professionals in the field. What the framework promises feels 'too good to be true'. Is it?










      share|cite|improve this question









      New contributor




      neutonquestiononetimeuser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      I came across an article about new proprietary 'Neuton' framework.



      I can't find educated opinions from professionals in the field. What the framework promises feels 'too good to be true'. Is it?







      machine-learning neural-networks






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      edited 4 hours ago









      Sven Hohenstein

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      asked 4 hours ago









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          There is not enough information freely available to make an intelligent discussion on the topic.



          That said, do not take any benchmarks seriously unless coming from an independent and authoritative source. For example, on Neuton's homepage benchmarks, XGBoost is always better than Catboost; but if we check Yandex's arXiv paper on Catboost, Catboost is always better than XGBoost. Clearly, these two scenarios are contradictory on face value! While I do not believe anyone intentionally obscures the truth, reality might be a bit too complex to justify statements like: "in the vast majority of cases, (algorithm X) achieves a higher level of accuracy than algorithms traditionally used for regression and classification problems." The marketing department might had a say there too... ;)



          Don't stop studying ML just yet.






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          • (For the record I have voted this question to close as primarily opinion-based.)
            – usεr11852
            31 mins ago










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          1 Answer
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          1 Answer
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          up vote
          2
          down vote













          There is not enough information freely available to make an intelligent discussion on the topic.



          That said, do not take any benchmarks seriously unless coming from an independent and authoritative source. For example, on Neuton's homepage benchmarks, XGBoost is always better than Catboost; but if we check Yandex's arXiv paper on Catboost, Catboost is always better than XGBoost. Clearly, these two scenarios are contradictory on face value! While I do not believe anyone intentionally obscures the truth, reality might be a bit too complex to justify statements like: "in the vast majority of cases, (algorithm X) achieves a higher level of accuracy than algorithms traditionally used for regression and classification problems." The marketing department might had a say there too... ;)



          Don't stop studying ML just yet.






          share|cite|improve this answer




















          • (For the record I have voted this question to close as primarily opinion-based.)
            – usεr11852
            31 mins ago














          up vote
          2
          down vote













          There is not enough information freely available to make an intelligent discussion on the topic.



          That said, do not take any benchmarks seriously unless coming from an independent and authoritative source. For example, on Neuton's homepage benchmarks, XGBoost is always better than Catboost; but if we check Yandex's arXiv paper on Catboost, Catboost is always better than XGBoost. Clearly, these two scenarios are contradictory on face value! While I do not believe anyone intentionally obscures the truth, reality might be a bit too complex to justify statements like: "in the vast majority of cases, (algorithm X) achieves a higher level of accuracy than algorithms traditionally used for regression and classification problems." The marketing department might had a say there too... ;)



          Don't stop studying ML just yet.






          share|cite|improve this answer




















          • (For the record I have voted this question to close as primarily opinion-based.)
            – usεr11852
            31 mins ago












          up vote
          2
          down vote










          up vote
          2
          down vote









          There is not enough information freely available to make an intelligent discussion on the topic.



          That said, do not take any benchmarks seriously unless coming from an independent and authoritative source. For example, on Neuton's homepage benchmarks, XGBoost is always better than Catboost; but if we check Yandex's arXiv paper on Catboost, Catboost is always better than XGBoost. Clearly, these two scenarios are contradictory on face value! While I do not believe anyone intentionally obscures the truth, reality might be a bit too complex to justify statements like: "in the vast majority of cases, (algorithm X) achieves a higher level of accuracy than algorithms traditionally used for regression and classification problems." The marketing department might had a say there too... ;)



          Don't stop studying ML just yet.






          share|cite|improve this answer












          There is not enough information freely available to make an intelligent discussion on the topic.



          That said, do not take any benchmarks seriously unless coming from an independent and authoritative source. For example, on Neuton's homepage benchmarks, XGBoost is always better than Catboost; but if we check Yandex's arXiv paper on Catboost, Catboost is always better than XGBoost. Clearly, these two scenarios are contradictory on face value! While I do not believe anyone intentionally obscures the truth, reality might be a bit too complex to justify statements like: "in the vast majority of cases, (algorithm X) achieves a higher level of accuracy than algorithms traditionally used for regression and classification problems." The marketing department might had a say there too... ;)



          Don't stop studying ML just yet.







          share|cite|improve this answer












          share|cite|improve this answer



          share|cite|improve this answer










          answered 36 mins ago









          usεr11852

          17k13670




          17k13670











          • (For the record I have voted this question to close as primarily opinion-based.)
            – usεr11852
            31 mins ago
















          • (For the record I have voted this question to close as primarily opinion-based.)
            – usεr11852
            31 mins ago















          (For the record I have voted this question to close as primarily opinion-based.)
          – usεr11852
          31 mins ago




          (For the record I have voted this question to close as primarily opinion-based.)
          – usεr11852
          31 mins ago










          neutonquestiononetimeuser is a new contributor. Be nice, and check out our Code of Conduct.









           

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