What does Cross-Validation do (Data Science)?
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I am confused about what cross-validation is for:
a) model optimization or
b) model validation?
Some good online courses mention that cross validation can be used to select the best model after folding K times, which would be a).
But I believe that this is incorrect. In my experience, it is for validation and it averages scores. Given that Udacity as a source seems to be pretty credible, I wonder if I am wrong. Am I missing something here?
statistics data-analysis
asked Jan 13 at 1:35
Expat CExpat C
14925
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add a comment |
$begingroup$
I am confused about what cross-validation is for:
a) model optimization or
b) model validation?
Some good online courses mention that cross validation can be used to select the best model after folding K times, which would be a).
But I believe that this is incorrect. In my experience, it is for validation and it averages scores. Given that Udacity as a source seems to be pretty credible, I wonder if I am wrong. Am I missing something here?
statistics data-analysis
asked Jan 13 at 1:35
Expat CExpat C
14925
$endgroup$
1
$begingroup$
Source: youtube.com/watch?v=sFO2ff-gTh0
$endgroup$
– Expat C
Jan 13 at 1:36
2
$begingroup$
If you don't get a good answer here, you could try stats.stackexchange.com which calls itself Cross Validated.
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– Gerry Myerson
Jan 13 at 1:43
1
$begingroup$
Cross-validation gives you a way to estimate the test error for your model. This capability can be used to compare various models and select the best one. That means you can try out various hyperparameter values and see which one works best.
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– littleO
Jan 13 at 1:44
1
$begingroup$
I believe you need to first have cristal clear what overfittig is, then cross validation application become obvious
$endgroup$
– Tommaso Bendinelli
Jan 13 at 1:49
add a comment |
$begingroup$
I am confused about what cross-validation is for:
a) model optimization or
b) model validation?
Some good online courses mention that cross validation can be used to select the best model after folding K times, which would be a).
But I believe that this is incorrect. In my experience, it is for validation and it averages scores. Given that Udacity as a source seems to be pretty credible, I wonder if I am wrong. Am I missing something here?
statistics data-analysis
asked Jan 13 at 1:35
Expat CExpat C
14925
$endgroup$
I am confused about what cross-validation is for:
a) model optimization or
b) model validation?
Some good online courses mention that cross validation can be used to select the best model after folding K times, which would be a).
But I believe that this is incorrect. In my experience, it is for validation and it averages scores. Given that Udacity as a source seems to be pretty credible, I wonder if I am wrong. Am I missing something here?
statistics data-analysis
statistics data-analysis
asked Jan 13 at 1:35
Expat CExpat C
14925
asked Jan 13 at 1:35
Expat CExpat C
14925
asked Jan 13 at 1:35
Expat CExpat C
14925
asked Jan 13 at 1:35
Expat CExpat C
14925
asked Jan 13 at 1:35
Expat CExpat C
14925
14925
1
$begingroup$
Source: youtube.com/watch?v=sFO2ff-gTh0
$endgroup$
– Expat C
Jan 13 at 1:36
2
$begingroup$
If you don't get a good answer here, you could try stats.stackexchange.com which calls itself Cross Validated.
$endgroup$
– Gerry Myerson
Jan 13 at 1:43
1
$begingroup$
Cross-validation gives you a way to estimate the test error for your model. This capability can be used to compare various models and select the best one. That means you can try out various hyperparameter values and see which one works best.
$endgroup$
– littleO
Jan 13 at 1:44
1
$begingroup$
I believe you need to first have cristal clear what overfittig is, then cross validation application become obvious
$endgroup$
– Tommaso Bendinelli
Jan 13 at 1:49
add a comment |
1
$begingroup$
Source: youtube.com/watch?v=sFO2ff-gTh0
$endgroup$
– Expat C
Jan 13 at 1:36
2
$begingroup$
If you don't get a good answer here, you could try stats.stackexchange.com which calls itself Cross Validated.
$endgroup$
– Gerry Myerson
Jan 13 at 1:43
1
$begingroup$
Cross-validation gives you a way to estimate the test error for your model. This capability can be used to compare various models and select the best one. That means you can try out various hyperparameter values and see which one works best.
$endgroup$
– littleO
Jan 13 at 1:44
1
$begingroup$
I believe you need to first have cristal clear what overfittig is, then cross validation application become obvious
$endgroup$
– Tommaso Bendinelli
Jan 13 at 1:49
1
1
$begingroup$
Source: youtube.com/watch?v=sFO2ff-gTh0
$endgroup$
– Expat C
Jan 13 at 1:36
$begingroup$
Source: youtube.com/watch?v=sFO2ff-gTh0
$endgroup$
– Expat C
Jan 13 at 1:36
2
2
$begingroup$
If you don't get a good answer here, you could try stats.stackexchange.com which calls itself Cross Validated.
$endgroup$
– Gerry Myerson
Jan 13 at 1:43
$begingroup$
If you don't get a good answer here, you could try stats.stackexchange.com which calls itself Cross Validated.
$endgroup$
– Gerry Myerson
Jan 13 at 1:43
1
1
$begingroup$
Cross-validation gives you a way to estimate the test error for your model. This capability can be used to compare various models and select the best one. That means you can try out various hyperparameter values and see which one works best.
$endgroup$
– littleO
Jan 13 at 1:44
$begingroup$
Cross-validation gives you a way to estimate the test error for your model. This capability can be used to compare various models and select the best one. That means you can try out various hyperparameter values and see which one works best.
$endgroup$
– littleO
Jan 13 at 1:44
1
1
$begingroup$
I believe you need to first have cristal clear what overfittig is, then cross validation application become obvious
$endgroup$
– Tommaso Bendinelli
Jan 13 at 1:49
$begingroup$
I believe you need to first have cristal clear what overfittig is, then cross validation application become obvious
$endgroup$
– Tommaso Bendinelli
Jan 13 at 1:49
add a comment |
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1
$begingroup$
Source: youtube.com/watch?v=sFO2ff-gTh0
$endgroup$
– Expat C
Jan 13 at 1:36
2
$begingroup$
If you don't get a good answer here, you could try stats.stackexchange.com which calls itself Cross Validated.
$endgroup$
– Gerry Myerson
Jan 13 at 1:43
1
$begingroup$
Cross-validation gives you a way to estimate the test error for your model. This capability can be used to compare various models and select the best one. That means you can try out various hyperparameter values and see which one works best.
$endgroup$
– littleO
Jan 13 at 1:44
1
$begingroup$
I believe you need to first have cristal clear what overfittig is, then cross validation application become obvious
$endgroup$
– Tommaso Bendinelli
Jan 13 at 1:49