scale_pos_weight Xgboost
$begingroup$
My question is rather simple what does the parameter scale_pos_weight in xgboost do? I know typically it should be $frac{sum(negative cases)}{sum(positive cases)}$.
Does it oversample the minority class by that ratio or does it undersample the majority class by inverse of that ratio? Or something else?
Also I would like to know if during cross validation in xgbcv, does the sampling happen on the test part of the cross-validation also or only the train part is affected by scale_pos_weight? Because i've heard that sampling should never be applied to test as it gives over-optimistic results.
classification xgboost unbalanced-classes
asked Jun 14 '17 at 10:32
Dhruv MahajanDhruv Mahajan
1788
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bumped to the homepage by Community♦ 18 hours 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 |
$begingroup$
My question is rather simple what does the parameter scale_pos_weight in xgboost do? I know typically it should be $frac{sum(negative cases)}{sum(positive cases)}$.
Does it oversample the minority class by that ratio or does it undersample the majority class by inverse of that ratio? Or something else?
Also I would like to know if during cross validation in xgbcv, does the sampling happen on the test part of the cross-validation also or only the train part is affected by scale_pos_weight? Because i've heard that sampling should never be applied to test as it gives over-optimistic results.
classification xgboost unbalanced-classes
asked Jun 14 '17 at 10:32
Dhruv MahajanDhruv Mahajan
1788
$endgroup$
bumped to the homepage by Community♦ 18 hours 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 |
$begingroup$
My question is rather simple what does the parameter scale_pos_weight in xgboost do? I know typically it should be $frac{sum(negative cases)}{sum(positive cases)}$.
Does it oversample the minority class by that ratio or does it undersample the majority class by inverse of that ratio? Or something else?
Also I would like to know if during cross validation in xgbcv, does the sampling happen on the test part of the cross-validation also or only the train part is affected by scale_pos_weight? Because i've heard that sampling should never be applied to test as it gives over-optimistic results.
classification xgboost unbalanced-classes
asked Jun 14 '17 at 10:32
Dhruv MahajanDhruv Mahajan
1788
$endgroup$
My question is rather simple what does the parameter scale_pos_weight in xgboost do? I know typically it should be $frac{sum(negative cases)}{sum(positive cases)}$.
Does it oversample the minority class by that ratio or does it undersample the majority class by inverse of that ratio? Or something else?
Also I would like to know if during cross validation in xgbcv, does the sampling happen on the test part of the cross-validation also or only the train part is affected by scale_pos_weight? Because i've heard that sampling should never be applied to test as it gives over-optimistic results.
classification xgboost unbalanced-classes
classification xgboost unbalanced-classes
asked Jun 14 '17 at 10:32
Dhruv MahajanDhruv Mahajan
1788
asked Jun 14 '17 at 10:32
Dhruv MahajanDhruv Mahajan
1788
asked Jun 14 '17 at 10:32
Dhruv MahajanDhruv Mahajan
1788
asked Jun 14 '17 at 10:32
Dhruv MahajanDhruv Mahajan
1788
asked Jun 14 '17 at 10:32
Dhruv MahajanDhruv Mahajan
1788
1788
bumped to the homepage by Community♦ 18 hours 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♦ 18 hours 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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$begingroup$
I guess while computing total error from errors of individual samples, if sample happens to be of a positive class, its error is multiplied by the scale_pos_weight factor. Therefore setting high scale_pos_weight urges optimizer to treat more important (mostly due to its rareness) class with more respect due its higher contribution to the total error value.
answered Jul 17 '18 at 22:02
Anatoly AlekseevAnatoly Alekseev
1011
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$begingroup$
I guess while computing total error from errors of individual samples, if sample happens to be of a positive class, its error is multiplied by the scale_pos_weight factor. Therefore setting high scale_pos_weight urges optimizer to treat more important (mostly due to its rareness) class with more respect due its higher contribution to the total error value.
answered Jul 17 '18 at 22:02
Anatoly AlekseevAnatoly Alekseev
1011
$endgroup$
add a comment |
$begingroup$
I guess while computing total error from errors of individual samples, if sample happens to be of a positive class, its error is multiplied by the scale_pos_weight factor. Therefore setting high scale_pos_weight urges optimizer to treat more important (mostly due to its rareness) class with more respect due its higher contribution to the total error value.
answered Jul 17 '18 at 22:02
Anatoly AlekseevAnatoly Alekseev
1011
$endgroup$
add a comment |
$begingroup$
I guess while computing total error from errors of individual samples, if sample happens to be of a positive class, its error is multiplied by the scale_pos_weight factor. Therefore setting high scale_pos_weight urges optimizer to treat more important (mostly due to its rareness) class with more respect due its higher contribution to the total error value.
answered Jul 17 '18 at 22:02
Anatoly AlekseevAnatoly Alekseev
1011
$endgroup$
I guess while computing total error from errors of individual samples, if sample happens to be of a positive class, its error is multiplied by the scale_pos_weight factor. Therefore setting high scale_pos_weight urges optimizer to treat more important (mostly due to its rareness) class with more respect due its higher contribution to the total error value.
answered Jul 17 '18 at 22:02
Anatoly AlekseevAnatoly Alekseev
1011
answered Jul 17 '18 at 22:02
Anatoly AlekseevAnatoly Alekseev
1011
answered Jul 17 '18 at 22:02
Anatoly AlekseevAnatoly Alekseev
1011
answered Jul 17 '18 at 22:02
Anatoly AlekseevAnatoly Alekseev
1011
1011
add a comment |
add a comment |
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