Unbalanced multiclass data with XGBoost
$begingroup$
I have 3 classes with this distribution:
Class 0: 0.1169
Class 1: 0.7668
Class 2: 0.1163
And I am using xgboost for classification. I know that there is a parameter called scale_pos_weight.
But how is it handled for 'multiclass' case, and how can I properly set it?
classification xgboost multiclass-classification unbalanced-classes
edited Dec 30 '18 at 14:29
рüффп
205415
asked Jan 16 '17 at 12:53
shdashda
185210
$endgroup$
add a comment |
$begingroup$
I have 3 classes with this distribution:
Class 0: 0.1169
Class 1: 0.7668
Class 2: 0.1163
And I am using xgboost for classification. I know that there is a parameter called scale_pos_weight.
But how is it handled for 'multiclass' case, and how can I properly set it?
classification xgboost multiclass-classification unbalanced-classes
edited Dec 30 '18 at 14:29
рüффп
205415
asked Jan 16 '17 at 12:53
shdashda
185210
$endgroup$
add a comment |
$begingroup$
I have 3 classes with this distribution:
Class 0: 0.1169
Class 1: 0.7668
Class 2: 0.1163
And I am using xgboost for classification. I know that there is a parameter called scale_pos_weight.
But how is it handled for 'multiclass' case, and how can I properly set it?
classification xgboost multiclass-classification unbalanced-classes
edited Dec 30 '18 at 14:29
рüффп
205415
asked Jan 16 '17 at 12:53
shdashda
185210
$endgroup$
I have 3 classes with this distribution:
Class 0: 0.1169
Class 1: 0.7668
Class 2: 0.1163
And I am using xgboost for classification. I know that there is a parameter called scale_pos_weight.
But how is it handled for 'multiclass' case, and how can I properly set it?
classification xgboost multiclass-classification unbalanced-classes
classification xgboost multiclass-classification unbalanced-classes
edited Dec 30 '18 at 14:29
рüффп
205415
asked Jan 16 '17 at 12:53
shdashda
185210
edited Dec 30 '18 at 14:29
рüффп
205415
asked Jan 16 '17 at 12:53
shdashda
185210
edited Dec 30 '18 at 14:29
рüффп
205415
edited Dec 30 '18 at 14:29
рüффп
205415
edited Dec 30 '18 at 14:29
рüффп
205415
205415
asked Jan 16 '17 at 12:53
shdashda
185210
asked Jan 16 '17 at 12:53
shdashda
185210
asked Jan 16 '17 at 12:53
shdashda
185210
185210
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
$begingroup$
scale_pos_weight is used for binary classification as you stated. It is a more generalized solution to handle imbalanced classes. A good approach when assigning a value to scale_pos_weight is:
sum(negative instances) / sum(positive instances)
For your specific case, there is another option in order to weight individual data points and take their weights into account while working with the booster, and let the optimization happen regarding their weights so that each point is represented equally. You just need to simply use:
xgboost.DMatrix(..., weight = *weight array for individual weights*)
You can define the weights as you like and by doing so, you can even handle imbalances within classes as well as imbalances across different classes.
edited Aug 26 '17 at 8:40
bstockton
1578
answered May 8 '17 at 9:42
Kerem TKerem T
16123
$endgroup$
add a comment |
$begingroup$
Everyone stumbles upon this question when dealing with unbalanced multiclass classification problem using XGBoost in R. I did too!
I was looking for an example to better understand how to apply it. Invested almost an hour to find the link mentioned below. For all those who are looking for an example, here goes -
https://datascience.stackexchange.com/a/9493/37156
Thanks wacax
answered Feb 25 '18 at 13:27
Krithi07Krithi07
4117
$endgroup$
add a comment |
$begingroup$
This answer by @KeremT is correct. I provide an example for those who still have problems with the exact implementation.
weight parameter in XGBoost is per instance not per class. Therefore, we need to assign the weight of each class to its instances, which is the same thing.
For example, if we have three imbalanced classes with ratios
class A = 10%
class B = 30%
class C = 60%
Their weights would be (dividing the smallest class by others)
class A = 1.000
class B = 0.333
class C = 0.167
Then, if training data is
index class
0 A
1 A
2 B
3 C
4 B
we build the weight vector as follows:
index class weight
0 A 1.000
1 A 1.000
2 B 0.333
3 C 0.167
4 B 0.333
answered yesterday
EsmailianEsmailian
3,021320
$endgroup$
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
scale_pos_weight is used for binary classification as you stated. It is a more generalized solution to handle imbalanced classes. A good approach when assigning a value to scale_pos_weight is:
sum(negative instances) / sum(positive instances)
For your specific case, there is another option in order to weight individual data points and take their weights into account while working with the booster, and let the optimization happen regarding their weights so that each point is represented equally. You just need to simply use:
xgboost.DMatrix(..., weight = *weight array for individual weights*)
You can define the weights as you like and by doing so, you can even handle imbalances within classes as well as imbalances across different classes.
edited Aug 26 '17 at 8:40
bstockton
1578
answered May 8 '17 at 9:42
Kerem TKerem T
16123
$endgroup$
add a comment |
$begingroup$
scale_pos_weight is used for binary classification as you stated. It is a more generalized solution to handle imbalanced classes. A good approach when assigning a value to scale_pos_weight is:
sum(negative instances) / sum(positive instances)
For your specific case, there is another option in order to weight individual data points and take their weights into account while working with the booster, and let the optimization happen regarding their weights so that each point is represented equally. You just need to simply use:
xgboost.DMatrix(..., weight = *weight array for individual weights*)
You can define the weights as you like and by doing so, you can even handle imbalances within classes as well as imbalances across different classes.
edited Aug 26 '17 at 8:40
bstockton
1578
answered May 8 '17 at 9:42
Kerem TKerem T
16123
$endgroup$
add a comment |
$begingroup$
scale_pos_weight is used for binary classification as you stated. It is a more generalized solution to handle imbalanced classes. A good approach when assigning a value to scale_pos_weight is:
sum(negative instances) / sum(positive instances)
For your specific case, there is another option in order to weight individual data points and take their weights into account while working with the booster, and let the optimization happen regarding their weights so that each point is represented equally. You just need to simply use:
xgboost.DMatrix(..., weight = *weight array for individual weights*)
You can define the weights as you like and by doing so, you can even handle imbalances within classes as well as imbalances across different classes.
edited Aug 26 '17 at 8:40
bstockton
1578
answered May 8 '17 at 9:42
Kerem TKerem T
16123
$endgroup$
scale_pos_weight is used for binary classification as you stated. It is a more generalized solution to handle imbalanced classes. A good approach when assigning a value to scale_pos_weight is:
sum(negative instances) / sum(positive instances)
For your specific case, there is another option in order to weight individual data points and take their weights into account while working with the booster, and let the optimization happen regarding their weights so that each point is represented equally. You just need to simply use:
xgboost.DMatrix(..., weight = *weight array for individual weights*)
You can define the weights as you like and by doing so, you can even handle imbalances within classes as well as imbalances across different classes.
edited Aug 26 '17 at 8:40
bstockton
1578
answered May 8 '17 at 9:42
Kerem TKerem T
16123
edited Aug 26 '17 at 8:40
bstockton
1578
edited Aug 26 '17 at 8:40
bstockton
1578
edited Aug 26 '17 at 8:40
bstockton
1578
1578
answered May 8 '17 at 9:42
Kerem TKerem T
16123
answered May 8 '17 at 9:42
Kerem TKerem T
16123
answered May 8 '17 at 9:42
Kerem TKerem T
16123
16123
add a comment |
add a comment |
$begingroup$
Everyone stumbles upon this question when dealing with unbalanced multiclass classification problem using XGBoost in R. I did too!
I was looking for an example to better understand how to apply it. Invested almost an hour to find the link mentioned below. For all those who are looking for an example, here goes -
https://datascience.stackexchange.com/a/9493/37156
Thanks wacax
answered Feb 25 '18 at 13:27
Krithi07Krithi07
4117
$endgroup$
add a comment |
$begingroup$
Everyone stumbles upon this question when dealing with unbalanced multiclass classification problem using XGBoost in R. I did too!
I was looking for an example to better understand how to apply it. Invested almost an hour to find the link mentioned below. For all those who are looking for an example, here goes -
https://datascience.stackexchange.com/a/9493/37156
Thanks wacax
answered Feb 25 '18 at 13:27
Krithi07Krithi07
4117
$endgroup$
add a comment |
$begingroup$
Everyone stumbles upon this question when dealing with unbalanced multiclass classification problem using XGBoost in R. I did too!
I was looking for an example to better understand how to apply it. Invested almost an hour to find the link mentioned below. For all those who are looking for an example, here goes -
https://datascience.stackexchange.com/a/9493/37156
Thanks wacax
answered Feb 25 '18 at 13:27
Krithi07Krithi07
4117
$endgroup$
Everyone stumbles upon this question when dealing with unbalanced multiclass classification problem using XGBoost in R. I did too!
I was looking for an example to better understand how to apply it. Invested almost an hour to find the link mentioned below. For all those who are looking for an example, here goes -
https://datascience.stackexchange.com/a/9493/37156
Thanks wacax
answered Feb 25 '18 at 13:27
Krithi07Krithi07
4117
answered Feb 25 '18 at 13:27
Krithi07Krithi07
4117
answered Feb 25 '18 at 13:27
Krithi07Krithi07
4117
answered Feb 25 '18 at 13:27
Krithi07Krithi07
4117
4117
add a comment |
add a comment |
$begingroup$
This answer by @KeremT is correct. I provide an example for those who still have problems with the exact implementation.
weight parameter in XGBoost is per instance not per class. Therefore, we need to assign the weight of each class to its instances, which is the same thing.
For example, if we have three imbalanced classes with ratios
class A = 10%
class B = 30%
class C = 60%
Their weights would be (dividing the smallest class by others)
class A = 1.000
class B = 0.333
class C = 0.167
Then, if training data is
index class
0 A
1 A
2 B
3 C
4 B
we build the weight vector as follows:
index class weight
0 A 1.000
1 A 1.000
2 B 0.333
3 C 0.167
4 B 0.333
answered yesterday
EsmailianEsmailian
3,021320
$endgroup$
add a comment |
$begingroup$
This answer by @KeremT is correct. I provide an example for those who still have problems with the exact implementation.
weight parameter in XGBoost is per instance not per class. Therefore, we need to assign the weight of each class to its instances, which is the same thing.
For example, if we have three imbalanced classes with ratios
class A = 10%
class B = 30%
class C = 60%
Their weights would be (dividing the smallest class by others)
class A = 1.000
class B = 0.333
class C = 0.167
Then, if training data is
index class
0 A
1 A
2 B
3 C
4 B
we build the weight vector as follows:
index class weight
0 A 1.000
1 A 1.000
2 B 0.333
3 C 0.167
4 B 0.333
answered yesterday
EsmailianEsmailian
3,021320
$endgroup$
add a comment |
$begingroup$
This answer by @KeremT is correct. I provide an example for those who still have problems with the exact implementation.
weight parameter in XGBoost is per instance not per class. Therefore, we need to assign the weight of each class to its instances, which is the same thing.
For example, if we have three imbalanced classes with ratios
class A = 10%
class B = 30%
class C = 60%
Their weights would be (dividing the smallest class by others)
class A = 1.000
class B = 0.333
class C = 0.167
Then, if training data is
index class
0 A
1 A
2 B
3 C
4 B
we build the weight vector as follows:
index class weight
0 A 1.000
1 A 1.000
2 B 0.333
3 C 0.167
4 B 0.333
answered yesterday
EsmailianEsmailian
3,021320
$endgroup$
This answer by @KeremT is correct. I provide an example for those who still have problems with the exact implementation.
weight parameter in XGBoost is per instance not per class. Therefore, we need to assign the weight of each class to its instances, which is the same thing.
For example, if we have three imbalanced classes with ratios
class A = 10%
class B = 30%
class C = 60%
Their weights would be (dividing the smallest class by others)
class A = 1.000
class B = 0.333
class C = 0.167
Then, if training data is
index class
0 A
1 A
2 B
3 C
4 B
we build the weight vector as follows:
index class weight
0 A 1.000
1 A 1.000
2 B 0.333
3 C 0.167
4 B 0.333
answered yesterday
EsmailianEsmailian
3,021320
edited yesterday
answered yesterday
EsmailianEsmailian
3,021320
answered yesterday
EsmailianEsmailian
3,021320
answered yesterday
EsmailianEsmailian
3,021320
3,021320
add a comment |
add a comment |
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