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?
machine-learning neural-network keras scikit-learn
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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?
machine-learning neural-network keras scikit-learn
New contributor
$endgroup$
add a comment |
$begingroup$
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?
machine-learning neural-network keras scikit-learn
New contributor
$endgroup$
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
machine-learning neural-network keras scikit-learn
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edited 5 mins ago
Chirag Gupta
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asked 20 mins ago
Chirag GuptaChirag Gupta
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