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
asked 21 hours ago
Chirag GuptaChirag Gupta
112
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.
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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
asked 21 hours ago
Chirag GuptaChirag Gupta
112
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.
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$begingroup$
The accuracy you are mentioning, is it on validation split or? If so, what is the accuracy on training split?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
Mentioned accuracy is on validation split
$endgroup$
– Chirag Gupta
18 hours ago
$begingroup$
What is the accuracy on training split in those two cases?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
Training loss and accuracy is almost the same in both cases. Goes till 100% if kept training. The rate of increase is also almost same for both cases (for training data)
$endgroup$
– Chirag Gupta
18 hours ago
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
asked 21 hours ago
Chirag GuptaChirag Gupta
112
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
machine-learning neural-network keras scikit-learn
asked 21 hours ago
Chirag GuptaChirag Gupta
112
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.
asked 21 hours ago
Chirag GuptaChirag Gupta
112
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.
edited 21 hours ago
Chirag Gupta
asked 21 hours ago
Chirag GuptaChirag Gupta
112
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.
asked 21 hours ago
Chirag GuptaChirag Gupta
112
asked 21 hours ago
Chirag GuptaChirag Gupta
112
112
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.
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.
Chirag Gupta is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
The accuracy you are mentioning, is it on validation split or? If so, what is the accuracy on training split?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
Mentioned accuracy is on validation split
$endgroup$
– Chirag Gupta
18 hours ago
$begingroup$
What is the accuracy on training split in those two cases?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
Training loss and accuracy is almost the same in both cases. Goes till 100% if kept training. The rate of increase is also almost same for both cases (for training data)
$endgroup$
– Chirag Gupta
18 hours ago
add a comment |
$begingroup$
The accuracy you are mentioning, is it on validation split or? If so, what is the accuracy on training split?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
Mentioned accuracy is on validation split
$endgroup$
– Chirag Gupta
18 hours ago
$begingroup$
What is the accuracy on training split in those two cases?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
Training loss and accuracy is almost the same in both cases. Goes till 100% if kept training. The rate of increase is also almost same for both cases (for training data)
$endgroup$
– Chirag Gupta
18 hours ago
$begingroup$
The accuracy you are mentioning, is it on validation split or? If so, what is the accuracy on training split?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
The accuracy you are mentioning, is it on validation split or? If so, what is the accuracy on training split?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
Mentioned accuracy is on validation split
$endgroup$
– Chirag Gupta
18 hours ago
$begingroup$
Mentioned accuracy is on validation split
$endgroup$
– Chirag Gupta
18 hours ago
$begingroup$
What is the accuracy on training split in those two cases?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
What is the accuracy on training split in those two cases?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
Training loss and accuracy is almost the same in both cases. Goes till 100% if kept training. The rate of increase is also almost same for both cases (for training data)
$endgroup$
– Chirag Gupta
18 hours ago
$begingroup$
Training loss and accuracy is almost the same in both cases. Goes till 100% if kept training. The rate of increase is also almost same for both cases (for training data)
$endgroup$
– Chirag Gupta
18 hours ago
add a comment |
1 Answer
1
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votes
$begingroup$
If this is split is a train/validation split (not a hold out test set) then you should be doing cross-validation. You are going to be overly optimistic about the performance of your model for this set of features and hyperparameters if you try to split it "just right". Cross-validation will give you a more accurate portrayal regardless of your split. If this is for a train/test split (test being a hold out test set), this is a very bad practice, since you are informing your decision on how to make the split based on the performance of the test set.
answered 12 hours ago
WesWes
31511
$endgroup$
add a comment |
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1 Answer
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$begingroup$
If this is split is a train/validation split (not a hold out test set) then you should be doing cross-validation. You are going to be overly optimistic about the performance of your model for this set of features and hyperparameters if you try to split it "just right". Cross-validation will give you a more accurate portrayal regardless of your split. If this is for a train/test split (test being a hold out test set), this is a very bad practice, since you are informing your decision on how to make the split based on the performance of the test set.
answered 12 hours ago
WesWes
31511
$endgroup$
add a comment |
$begingroup$
If this is split is a train/validation split (not a hold out test set) then you should be doing cross-validation. You are going to be overly optimistic about the performance of your model for this set of features and hyperparameters if you try to split it "just right". Cross-validation will give you a more accurate portrayal regardless of your split. If this is for a train/test split (test being a hold out test set), this is a very bad practice, since you are informing your decision on how to make the split based on the performance of the test set.
answered 12 hours ago
WesWes
31511
$endgroup$
add a comment |
$begingroup$
If this is split is a train/validation split (not a hold out test set) then you should be doing cross-validation. You are going to be overly optimistic about the performance of your model for this set of features and hyperparameters if you try to split it "just right". Cross-validation will give you a more accurate portrayal regardless of your split. If this is for a train/test split (test being a hold out test set), this is a very bad practice, since you are informing your decision on how to make the split based on the performance of the test set.
answered 12 hours ago
WesWes
31511
$endgroup$
If this is split is a train/validation split (not a hold out test set) then you should be doing cross-validation. You are going to be overly optimistic about the performance of your model for this set of features and hyperparameters if you try to split it "just right". Cross-validation will give you a more accurate portrayal regardless of your split. If this is for a train/test split (test being a hold out test set), this is a very bad practice, since you are informing your decision on how to make the split based on the performance of the test set.
answered 12 hours ago
WesWes
31511
answered 12 hours ago
WesWes
31511
answered 12 hours ago
WesWes
31511
answered 12 hours ago
WesWes
31511
31511
add a comment |
add a comment |
Chirag Gupta is a new contributor. Be nice, and check out our Code of Conduct.
Chirag Gupta is a new contributor. Be nice, and check out our Code of Conduct.
Chirag Gupta is a new contributor. Be nice, and check out our Code of Conduct.
Chirag Gupta is a new contributor. Be nice, and check out our Code of Conduct.
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$begingroup$
The accuracy you are mentioning, is it on validation split or? If so, what is the accuracy on training split?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
Mentioned accuracy is on validation split
$endgroup$
– Chirag Gupta
18 hours ago
$begingroup$
What is the accuracy on training split in those two cases?
$endgroup$
– Antonio Jurić
18 hours ago
$begingroup$
Training loss and accuracy is almost the same in both cases. Goes till 100% if kept training. The rate of increase is also almost same for both cases (for training data)
$endgroup$
– Chirag Gupta
18 hours ago