emphasise some observation weights more than the others
$begingroup$
I want to emphasise (increase the weight) of only a subset of data. Lets say I have old and fresh data, I would like to say that old data has to have more weight and therefore has more influence in the decision than the new data.
In scikit-learn I found only class-weight parameter, but it does not change the weight of the samples, only of all samples within the class.
Is there a way to incorporate this emphasis into the gradient boosted trees in spark or xgboost in python?
weighted-data
asked Apr 16 '18 at 11:45
TonjaTonja
1033
$endgroup$
bumped to the homepage by Community♦ 18 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 want to emphasise (increase the weight) of only a subset of data. Lets say I have old and fresh data, I would like to say that old data has to have more weight and therefore has more influence in the decision than the new data.
In scikit-learn I found only class-weight parameter, but it does not change the weight of the samples, only of all samples within the class.
Is there a way to incorporate this emphasis into the gradient boosted trees in spark or xgboost in python?
weighted-data
asked Apr 16 '18 at 11:45
TonjaTonja
1033
$endgroup$
bumped to the homepage by Community♦ 18 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
2
$begingroup$
Are you sure it does that? The documentation suggests otherwise; seesample_weight_last_ten.
$endgroup$
– Emre
Apr 16 '18 at 20:34
add a comment |
$begingroup$
I want to emphasise (increase the weight) of only a subset of data. Lets say I have old and fresh data, I would like to say that old data has to have more weight and therefore has more influence in the decision than the new data.
In scikit-learn I found only class-weight parameter, but it does not change the weight of the samples, only of all samples within the class.
Is there a way to incorporate this emphasis into the gradient boosted trees in spark or xgboost in python?
weighted-data
asked Apr 16 '18 at 11:45
TonjaTonja
1033
$endgroup$
I want to emphasise (increase the weight) of only a subset of data. Lets say I have old and fresh data, I would like to say that old data has to have more weight and therefore has more influence in the decision than the new data.
In scikit-learn I found only class-weight parameter, but it does not change the weight of the samples, only of all samples within the class.
Is there a way to incorporate this emphasis into the gradient boosted trees in spark or xgboost in python?
weighted-data
weighted-data
asked Apr 16 '18 at 11:45
TonjaTonja
1033
asked Apr 16 '18 at 11:45
TonjaTonja
1033
asked Apr 16 '18 at 11:45
TonjaTonja
1033
asked Apr 16 '18 at 11:45
TonjaTonja
1033
asked Apr 16 '18 at 11:45
TonjaTonja
1033
1033
bumped to the homepage by Community♦ 18 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♦ 18 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
2
$begingroup$
Are you sure it does that? The documentation suggests otherwise; seesample_weight_last_ten.
$endgroup$
– Emre
Apr 16 '18 at 20:34
add a comment |
2
$begingroup$
Are you sure it does that? The documentation suggests otherwise; seesample_weight_last_ten.
$endgroup$
– Emre
Apr 16 '18 at 20:34
2
2
$begingroup$
Are you sure it does that? The documentation suggests otherwise; see
sample_weight_last_ten.$endgroup$
– Emre
Apr 16 '18 at 20:34
$begingroup$
Are you sure it does that? The documentation suggests otherwise; see
sample_weight_last_ten.$endgroup$
– Emre
Apr 16 '18 at 20:34
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
If you have a date variable (or something similar), you can create a weight using this.
If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.
answered May 17 '18 at 8:02
bradSbradS
667213
$endgroup$
add a comment |
$begingroup$
There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:
- A1
- A2
- B1
- B2
- C1
- C2
add:
- C1
- C2
answered Apr 16 '18 at 18:51
CalZCalZ
1,438213
$endgroup$
1
$begingroup$
You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
$endgroup$
– bradS
May 17 '18 at 8:00
add a comment |
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2 Answers
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active
oldest
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2 Answers
2
active
oldest
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votes
$begingroup$
If you have a date variable (or something similar), you can create a weight using this.
If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.
answered May 17 '18 at 8:02
bradSbradS
667213
$endgroup$
add a comment |
$begingroup$
If you have a date variable (or something similar), you can create a weight using this.
If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.
answered May 17 '18 at 8:02
bradSbradS
667213
$endgroup$
add a comment |
$begingroup$
If you have a date variable (or something similar), you can create a weight using this.
If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.
answered May 17 '18 at 8:02
bradSbradS
667213
$endgroup$
If you have a date variable (or something similar), you can create a weight using this.
If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.
answered May 17 '18 at 8:02
bradSbradS
667213
answered May 17 '18 at 8:02
bradSbradS
667213
answered May 17 '18 at 8:02
bradSbradS
667213
answered May 17 '18 at 8:02
bradSbradS
667213
667213
add a comment |
add a comment |
$begingroup$
There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:
- A1
- A2
- B1
- B2
- C1
- C2
add:
- C1
- C2
answered Apr 16 '18 at 18:51
CalZCalZ
1,438213
$endgroup$
1
$begingroup$
You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
$endgroup$
– bradS
May 17 '18 at 8:00
add a comment |
$begingroup$
There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:
- A1
- A2
- B1
- B2
- C1
- C2
add:
- C1
- C2
answered Apr 16 '18 at 18:51
CalZCalZ
1,438213
$endgroup$
1
$begingroup$
You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
$endgroup$
– bradS
May 17 '18 at 8:00
add a comment |
$begingroup$
There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:
- A1
- A2
- B1
- B2
- C1
- C2
add:
- C1
- C2
answered Apr 16 '18 at 18:51
CalZCalZ
1,438213
$endgroup$
There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:
- A1
- A2
- B1
- B2
- C1
- C2
add:
- C1
- C2
answered Apr 16 '18 at 18:51
CalZCalZ
1,438213
answered Apr 16 '18 at 18:51
CalZCalZ
1,438213
answered Apr 16 '18 at 18:51
CalZCalZ
1,438213
answered Apr 16 '18 at 18:51
CalZCalZ
1,438213
1,438213
1
$begingroup$
You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
$endgroup$
– bradS
May 17 '18 at 8:00
add a comment |
1
$begingroup$
You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
$endgroup$
– bradS
May 17 '18 at 8:00
1
1
$begingroup$
You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
$endgroup$
– bradS
May 17 '18 at 8:00
$begingroup$
You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
$endgroup$
– bradS
May 17 '18 at 8:00
add a comment |
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2
$begingroup$
Are you sure it does that? The documentation suggests otherwise; see
sample_weight_last_ten.$endgroup$
– Emre
Apr 16 '18 at 20:34