Is a good shuffle random state for training data really good for the model?












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I'm using keras to train a binary classifier neural network. To shuffle the training data I am using shuffle function from scikit-learn.

I observe that for some shuffle_random_state (seed for shuffle()), the network gives really good results (~86% accuracy) while on others not so much (~75% accuracy). So i run the model for 1-20 shuffle_random_states and choose the random_state which gives the best accuracy for production model.

I was wondering if this is a good approach and with those good shuffle_random_state the network is actually learning better?










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    $begingroup$


    I'm using keras to train a binary classifier neural network. To shuffle the training data I am using shuffle function from scikit-learn.

    I observe that for some shuffle_random_state (seed for shuffle()), the network gives really good results (~86% accuracy) while on others not so much (~75% accuracy). So i run the model for 1-20 shuffle_random_states and choose the random_state which gives the best accuracy for production model.

    I was wondering if this is a good approach and with those good shuffle_random_state the network is actually learning better?










    share|improve this question









    New contributor




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







    $endgroup$















      0












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      0





      $begingroup$


      I'm using keras to train a binary classifier neural network. To shuffle the training data I am using shuffle function from scikit-learn.

      I observe that for some shuffle_random_state (seed for shuffle()), the network gives really good results (~86% accuracy) while on others not so much (~75% accuracy). So i run the model for 1-20 shuffle_random_states and choose the random_state which gives the best accuracy for production model.

      I was wondering if this is a good approach and with those good shuffle_random_state the network is actually learning better?










      share|improve this question









      New contributor




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







      $endgroup$




      I'm using keras to train a binary classifier neural network. To shuffle the training data I am using shuffle function from scikit-learn.

      I observe that for some shuffle_random_state (seed for shuffle()), the network gives really good results (~86% accuracy) while on others not so much (~75% accuracy). So i run the model for 1-20 shuffle_random_states and choose the random_state which gives the best accuracy for production model.

      I was wondering if this is a good approach and with those good shuffle_random_state the network is actually learning better?







      machine-learning neural-network keras scikit-learn






      share|improve this question









      New contributor




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











      share|improve this question









      New contributor




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









      share|improve this question




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      edited 5 mins ago







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      asked 20 mins ago









      Chirag GuptaChirag Gupta

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