Nested cross-validation generalization error for multiple models
$begingroup$
I am referring to this question:
Nested cross-validation and selecting the best regression model - is this the right SKLearn process?
In the answers it shows that nested cv can estimate the generalization error of hyperparameter optimization for different algorithms.
But in my opinion the choice between different algorithms is also an optimization process, which leads to generalization errors. Therefore, either the algorithm choice should be part of the inner cv or another third cv would have to be introduced to evaluate the error for the algorithm choice. Is this a correct assumption ?
classification scikit-learn cross-validation machine-learning-model model-selection
edited Nov 7 '18 at 14:23
Kasra Manshaei
3,6391035
asked Nov 5 '18 at 13:39
Paul ZierepPaul Zierep
63
$endgroup$
bumped to the homepage by Community♦ 21 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I am referring to this question:
Nested cross-validation and selecting the best regression model - is this the right SKLearn process?
In the answers it shows that nested cv can estimate the generalization error of hyperparameter optimization for different algorithms.
But in my opinion the choice between different algorithms is also an optimization process, which leads to generalization errors. Therefore, either the algorithm choice should be part of the inner cv or another third cv would have to be introduced to evaluate the error for the algorithm choice. Is this a correct assumption ?
classification scikit-learn cross-validation machine-learning-model model-selection
edited Nov 7 '18 at 14:23
Kasra Manshaei
3,6391035
asked Nov 5 '18 at 13:39
Paul ZierepPaul Zierep
63
$endgroup$
bumped to the homepage by Community♦ 21 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I am referring to this question:
Nested cross-validation and selecting the best regression model - is this the right SKLearn process?
In the answers it shows that nested cv can estimate the generalization error of hyperparameter optimization for different algorithms.
But in my opinion the choice between different algorithms is also an optimization process, which leads to generalization errors. Therefore, either the algorithm choice should be part of the inner cv or another third cv would have to be introduced to evaluate the error for the algorithm choice. Is this a correct assumption ?
classification scikit-learn cross-validation machine-learning-model model-selection
edited Nov 7 '18 at 14:23
Kasra Manshaei
3,6391035
asked Nov 5 '18 at 13:39
Paul ZierepPaul Zierep
63
$endgroup$
I am referring to this question:
Nested cross-validation and selecting the best regression model - is this the right SKLearn process?
In the answers it shows that nested cv can estimate the generalization error of hyperparameter optimization for different algorithms.
But in my opinion the choice between different algorithms is also an optimization process, which leads to generalization errors. Therefore, either the algorithm choice should be part of the inner cv or another third cv would have to be introduced to evaluate the error for the algorithm choice. Is this a correct assumption ?
classification scikit-learn cross-validation machine-learning-model model-selection
classification scikit-learn cross-validation machine-learning-model model-selection
edited Nov 7 '18 at 14:23
Kasra Manshaei
3,6391035
asked Nov 5 '18 at 13:39
Paul ZierepPaul Zierep
63
edited Nov 7 '18 at 14:23
Kasra Manshaei
3,6391035
asked Nov 5 '18 at 13:39
Paul ZierepPaul Zierep
63
edited Nov 7 '18 at 14:23
Kasra Manshaei
3,6391035
edited Nov 7 '18 at 14:23
Kasra Manshaei
3,6391035
edited Nov 7 '18 at 14:23
Kasra Manshaei
3,6391035
3,6391035
asked Nov 5 '18 at 13:39
Paul ZierepPaul Zierep
63
asked Nov 5 '18 at 13:39
Paul ZierepPaul Zierep
63
asked Nov 5 '18 at 13:39
Paul ZierepPaul Zierep
63
63
bumped to the homepage by Community♦ 21 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 21 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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$begingroup$
In general you are right and in this answer it has been done as far as I see. The models are compared to each other while the best tuning of them is found, both inside the loop. It looks fine.
About your point, yes. But the point in Machine learning is that at some point we need to stop/limit our attempts as the number of algorithms which can do the task are very large. We usually try to evaluate different families of algorithms and then narrow the search from there but at the end we can never claim that the best answer we found is necessarily the best possible answer. In another POV, this is the main idea behind many research papers in ML. They just creatively find/modify an algorithm and show that it works better than previously applied algorithm through a benchmark dataset.
answered Nov 7 '18 at 14:29
Kasra ManshaeiKasra Manshaei
3,6391035
$endgroup$
add a comment |
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1 Answer
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oldest
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1 Answer
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active
oldest
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$begingroup$
In general you are right and in this answer it has been done as far as I see. The models are compared to each other while the best tuning of them is found, both inside the loop. It looks fine.
About your point, yes. But the point in Machine learning is that at some point we need to stop/limit our attempts as the number of algorithms which can do the task are very large. We usually try to evaluate different families of algorithms and then narrow the search from there but at the end we can never claim that the best answer we found is necessarily the best possible answer. In another POV, this is the main idea behind many research papers in ML. They just creatively find/modify an algorithm and show that it works better than previously applied algorithm through a benchmark dataset.
answered Nov 7 '18 at 14:29
Kasra ManshaeiKasra Manshaei
3,6391035
$endgroup$
add a comment |
$begingroup$
In general you are right and in this answer it has been done as far as I see. The models are compared to each other while the best tuning of them is found, both inside the loop. It looks fine.
About your point, yes. But the point in Machine learning is that at some point we need to stop/limit our attempts as the number of algorithms which can do the task are very large. We usually try to evaluate different families of algorithms and then narrow the search from there but at the end we can never claim that the best answer we found is necessarily the best possible answer. In another POV, this is the main idea behind many research papers in ML. They just creatively find/modify an algorithm and show that it works better than previously applied algorithm through a benchmark dataset.
answered Nov 7 '18 at 14:29
Kasra ManshaeiKasra Manshaei
3,6391035
$endgroup$
add a comment |
$begingroup$
In general you are right and in this answer it has been done as far as I see. The models are compared to each other while the best tuning of them is found, both inside the loop. It looks fine.
About your point, yes. But the point in Machine learning is that at some point we need to stop/limit our attempts as the number of algorithms which can do the task are very large. We usually try to evaluate different families of algorithms and then narrow the search from there but at the end we can never claim that the best answer we found is necessarily the best possible answer. In another POV, this is the main idea behind many research papers in ML. They just creatively find/modify an algorithm and show that it works better than previously applied algorithm through a benchmark dataset.
answered Nov 7 '18 at 14:29
Kasra ManshaeiKasra Manshaei
3,6391035
$endgroup$
In general you are right and in this answer it has been done as far as I see. The models are compared to each other while the best tuning of them is found, both inside the loop. It looks fine.
About your point, yes. But the point in Machine learning is that at some point we need to stop/limit our attempts as the number of algorithms which can do the task are very large. We usually try to evaluate different families of algorithms and then narrow the search from there but at the end we can never claim that the best answer we found is necessarily the best possible answer. In another POV, this is the main idea behind many research papers in ML. They just creatively find/modify an algorithm and show that it works better than previously applied algorithm through a benchmark dataset.
answered Nov 7 '18 at 14:29
Kasra ManshaeiKasra Manshaei
3,6391035
answered Nov 7 '18 at 14:29
Kasra ManshaeiKasra Manshaei
3,6391035
answered Nov 7 '18 at 14:29
Kasra ManshaeiKasra Manshaei
3,6391035
answered Nov 7 '18 at 14:29
Kasra ManshaeiKasra Manshaei
3,6391035
3,6391035
add a comment |
add a comment |
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