Why would one crossvalidate the random state number?Linear kernel in SVM performing much worse than RBF or PolyWhy is the number of samples smaller than the number of values in my decision tree?How does one fine-tune parameters and weights at the same time?Predicting contract churn/cancellation: Great model results does not work in the real worldWhy is this Random Forest perfect?Why would a fake feature with random numbers get selected in feature importance?Random state in machine learning modelsIs a good shuffle random state for training data really good for the model?Why is the reported loss different from the mean squared error calculated on the train data?Why is my MLP with 2 features is doing worse than MLP with 1 feature where the one feature is a combination of feature1*feature2?
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Why would one crossvalidate the random state number?
Linear kernel in SVM performing much worse than RBF or PolyWhy is the number of samples smaller than the number of values in my decision tree?How does one fine-tune parameters and weights at the same time?Predicting contract churn/cancellation: Great model results does not work in the real worldWhy is this Random Forest perfect?Why would a fake feature with random numbers get selected in feature importance?Random state in machine learning modelsIs a good shuffle random state for training data really good for the model?Why is the reported loss different from the mean squared error calculated on the train data?Why is my MLP with 2 features is doing worse than MLP with 1 feature where the one feature is a combination of feature1*feature2?
$begingroup$
Still learning about machine learning, I've stumbled across a kaggle (link) which I cannot understand.
Here are the lines 72 and 73:
parameters = 'solver': ['lbfgs'],
'max_iter': [1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000 ],
'alpha': 10.0 ** -np.arange(1, 10),
'hidden_layer_sizes':np.arange(10, 15),
'random_state':[0,1,2,3,4,5,6,7,8,9]
clf = GridSearchCV(MLPClassifier(), parameters, n_jobs=-1)
As you can see, the random_state parameter is been tested across 10 values.
What is the point of doing this?
If one model perform better with some random_state, does it make any sense to use this particular parameter on other models?
scikit-learn mlp
asked 3 hours ago
Dan ChaltielDan Chaltiel
1707
$endgroup$
add a comment |
$begingroup$
Still learning about machine learning, I've stumbled across a kaggle (link) which I cannot understand.
Here are the lines 72 and 73:
parameters = 'solver': ['lbfgs'],
'max_iter': [1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000 ],
'alpha': 10.0 ** -np.arange(1, 10),
'hidden_layer_sizes':np.arange(10, 15),
'random_state':[0,1,2,3,4,5,6,7,8,9]
clf = GridSearchCV(MLPClassifier(), parameters, n_jobs=-1)
As you can see, the random_state parameter is been tested across 10 values.
What is the point of doing this?
If one model perform better with some random_state, does it make any sense to use this particular parameter on other models?
scikit-learn mlp
asked 3 hours ago
Dan ChaltielDan Chaltiel
1707
$endgroup$
add a comment |
$begingroup$
Still learning about machine learning, I've stumbled across a kaggle (link) which I cannot understand.
Here are the lines 72 and 73:
parameters = 'solver': ['lbfgs'],
'max_iter': [1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000 ],
'alpha': 10.0 ** -np.arange(1, 10),
'hidden_layer_sizes':np.arange(10, 15),
'random_state':[0,1,2,3,4,5,6,7,8,9]
clf = GridSearchCV(MLPClassifier(), parameters, n_jobs=-1)
As you can see, the random_state parameter is been tested across 10 values.
What is the point of doing this?
If one model perform better with some random_state, does it make any sense to use this particular parameter on other models?
scikit-learn mlp
asked 3 hours ago
Dan ChaltielDan Chaltiel
1707
$endgroup$
Still learning about machine learning, I've stumbled across a kaggle (link) which I cannot understand.
Here are the lines 72 and 73:
parameters = 'solver': ['lbfgs'],
'max_iter': [1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000 ],
'alpha': 10.0 ** -np.arange(1, 10),
'hidden_layer_sizes':np.arange(10, 15),
'random_state':[0,1,2,3,4,5,6,7,8,9]
clf = GridSearchCV(MLPClassifier(), parameters, n_jobs=-1)
As you can see, the random_state parameter is been tested across 10 values.
What is the point of doing this?
If one model perform better with some random_state, does it make any sense to use this particular parameter on other models?
scikit-learn mlp
scikit-learn mlp
asked 3 hours ago
Dan ChaltielDan Chaltiel
1707
asked 3 hours ago
Dan ChaltielDan Chaltiel
1707
asked 3 hours ago
Dan ChaltielDan Chaltiel
1707
asked 3 hours ago
Dan ChaltielDan Chaltiel
1707
asked 3 hours ago
Dan ChaltielDan Chaltiel
1707
1707
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
I personally think that the general idea of optimising your model with different random seeds is not a good idea. There are many other, more important, aspects of the modelling process that you can worry about, tweak and compare before spending time on the effects of random initialisation.
That being said, if you just want to test the effect of random initialisation of model weights on a final validation metric, this could be an approach to do so. Kind of the reverse argument to my point above. If you can show for different random seeds (ceteris paribus: with all other parameters equal) that the final model performs differently, it shows maybe that their is either inconsistency in the model, or a bug in the code even. I would not expect a well-validated model to give hugely differing results if being run with a different random seed, so if it does, it tells me something weird is going on!
answered 3 hours ago
n1k31t4n1k31t4
6,9462422
$endgroup$
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
I personally think that the general idea of optimising your model with different random seeds is not a good idea. There are many other, more important, aspects of the modelling process that you can worry about, tweak and compare before spending time on the effects of random initialisation.
That being said, if you just want to test the effect of random initialisation of model weights on a final validation metric, this could be an approach to do so. Kind of the reverse argument to my point above. If you can show for different random seeds (ceteris paribus: with all other parameters equal) that the final model performs differently, it shows maybe that their is either inconsistency in the model, or a bug in the code even. I would not expect a well-validated model to give hugely differing results if being run with a different random seed, so if it does, it tells me something weird is going on!
answered 3 hours ago
n1k31t4n1k31t4
6,9462422
$endgroup$
add a comment |
$begingroup$
I personally think that the general idea of optimising your model with different random seeds is not a good idea. There are many other, more important, aspects of the modelling process that you can worry about, tweak and compare before spending time on the effects of random initialisation.
That being said, if you just want to test the effect of random initialisation of model weights on a final validation metric, this could be an approach to do so. Kind of the reverse argument to my point above. If you can show for different random seeds (ceteris paribus: with all other parameters equal) that the final model performs differently, it shows maybe that their is either inconsistency in the model, or a bug in the code even. I would not expect a well-validated model to give hugely differing results if being run with a different random seed, so if it does, it tells me something weird is going on!
answered 3 hours ago
n1k31t4n1k31t4
6,9462422
$endgroup$
add a comment |
$begingroup$
I personally think that the general idea of optimising your model with different random seeds is not a good idea. There are many other, more important, aspects of the modelling process that you can worry about, tweak and compare before spending time on the effects of random initialisation.
That being said, if you just want to test the effect of random initialisation of model weights on a final validation metric, this could be an approach to do so. Kind of the reverse argument to my point above. If you can show for different random seeds (ceteris paribus: with all other parameters equal) that the final model performs differently, it shows maybe that their is either inconsistency in the model, or a bug in the code even. I would not expect a well-validated model to give hugely differing results if being run with a different random seed, so if it does, it tells me something weird is going on!
answered 3 hours ago
n1k31t4n1k31t4
6,9462422
$endgroup$
I personally think that the general idea of optimising your model with different random seeds is not a good idea. There are many other, more important, aspects of the modelling process that you can worry about, tweak and compare before spending time on the effects of random initialisation.
That being said, if you just want to test the effect of random initialisation of model weights on a final validation metric, this could be an approach to do so. Kind of the reverse argument to my point above. If you can show for different random seeds (ceteris paribus: with all other parameters equal) that the final model performs differently, it shows maybe that their is either inconsistency in the model, or a bug in the code even. I would not expect a well-validated model to give hugely differing results if being run with a different random seed, so if it does, it tells me something weird is going on!
answered 3 hours ago
n1k31t4n1k31t4
6,9462422
answered 3 hours ago
n1k31t4n1k31t4
6,9462422
answered 3 hours ago
n1k31t4n1k31t4
6,9462422
answered 3 hours ago
n1k31t4n1k31t4
6,9462422
6,9462422
add a comment |
add a comment |
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