reusable holdout in mlr
How can someone change the cross validation or holdout procedures in mlr so that before testing with the validation set, that same validation set is changed according to a procedure, namely the reusable holdout procedure?
Procedure:
http://insilico.utulsa.edu/wp-content/uploads/2016/10/Dwork_2015_Science.pdf
r cross-validation mlr
asked Nov 22 '18 at 13:16
André PintoAndré Pinto
133
add a comment |
How can someone change the cross validation or holdout procedures in mlr so that before testing with the validation set, that same validation set is changed according to a procedure, namely the reusable holdout procedure?
Procedure:
http://insilico.utulsa.edu/wp-content/uploads/2016/10/Dwork_2015_Science.pdf
r cross-validation mlr
asked Nov 22 '18 at 13:16
André PintoAndré Pinto
133
add a comment |
How can someone change the cross validation or holdout procedures in mlr so that before testing with the validation set, that same validation set is changed according to a procedure, namely the reusable holdout procedure?
Procedure:
http://insilico.utulsa.edu/wp-content/uploads/2016/10/Dwork_2015_Science.pdf
r cross-validation mlr
asked Nov 22 '18 at 13:16
André PintoAndré Pinto
133
How can someone change the cross validation or holdout procedures in mlr so that before testing with the validation set, that same validation set is changed according to a procedure, namely the reusable holdout procedure?
Procedure:
http://insilico.utulsa.edu/wp-content/uploads/2016/10/Dwork_2015_Science.pdf
r cross-validation mlr
r cross-validation mlr
asked Nov 22 '18 at 13:16
André PintoAndré Pinto
133
asked Nov 22 '18 at 13:16
André PintoAndré Pinto
133
asked Nov 22 '18 at 13:16
André PintoAndré Pinto
133
asked Nov 22 '18 at 13:16
André PintoAndré Pinto
133
asked Nov 22 '18 at 13:16
André PintoAndré Pinto
133
133
add a comment |
add a comment |
1 Answer
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Short answer: mlr doesn't support that.
Long answer: My experience with differential privacy for machine learning is that in practice it doesn't work as well as advertised. In particular, to apply thresholdout you need a) copious amounts of data and b) the a priori probability that a given classifier will overfit on the given data -- something you can't easily determine in practice. While the paper you reference comes with example code that shows that thresholdout works in this particular case, but the amount of noise added in the code looks like it was determined on an ad-hoc basis; the relationship to the thresholdout algorithm described in the paper isn't clear.
Before differential privacy can be robustly applied in practice in scenarios like that, mlr won't support it.
answered Nov 22 '18 at 18:15
Lars KotthoffLars Kotthoff
90.5k11158165
Thank you for your answer, is there any alternative anyone tried to the method they describe that doesn't need the copious amount of data you describe?
– André Pinto
Nov 23 '18 at 19:06
Not that I'm aware of.
– Lars Kotthoff
Nov 23 '18 at 19:42
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Short answer: mlr doesn't support that.
Long answer: My experience with differential privacy for machine learning is that in practice it doesn't work as well as advertised. In particular, to apply thresholdout you need a) copious amounts of data and b) the a priori probability that a given classifier will overfit on the given data -- something you can't easily determine in practice. While the paper you reference comes with example code that shows that thresholdout works in this particular case, but the amount of noise added in the code looks like it was determined on an ad-hoc basis; the relationship to the thresholdout algorithm described in the paper isn't clear.
Before differential privacy can be robustly applied in practice in scenarios like that, mlr won't support it.
answered Nov 22 '18 at 18:15
Lars KotthoffLars Kotthoff
90.5k11158165
Thank you for your answer, is there any alternative anyone tried to the method they describe that doesn't need the copious amount of data you describe?
– André Pinto
Nov 23 '18 at 19:06
Not that I'm aware of.
– Lars Kotthoff
Nov 23 '18 at 19:42
add a comment |
Short answer: mlr doesn't support that.
Long answer: My experience with differential privacy for machine learning is that in practice it doesn't work as well as advertised. In particular, to apply thresholdout you need a) copious amounts of data and b) the a priori probability that a given classifier will overfit on the given data -- something you can't easily determine in practice. While the paper you reference comes with example code that shows that thresholdout works in this particular case, but the amount of noise added in the code looks like it was determined on an ad-hoc basis; the relationship to the thresholdout algorithm described in the paper isn't clear.
Before differential privacy can be robustly applied in practice in scenarios like that, mlr won't support it.
answered Nov 22 '18 at 18:15
Lars KotthoffLars Kotthoff
90.5k11158165
Thank you for your answer, is there any alternative anyone tried to the method they describe that doesn't need the copious amount of data you describe?
– André Pinto
Nov 23 '18 at 19:06
Not that I'm aware of.
– Lars Kotthoff
Nov 23 '18 at 19:42
add a comment |
Short answer: mlr doesn't support that.
Long answer: My experience with differential privacy for machine learning is that in practice it doesn't work as well as advertised. In particular, to apply thresholdout you need a) copious amounts of data and b) the a priori probability that a given classifier will overfit on the given data -- something you can't easily determine in practice. While the paper you reference comes with example code that shows that thresholdout works in this particular case, but the amount of noise added in the code looks like it was determined on an ad-hoc basis; the relationship to the thresholdout algorithm described in the paper isn't clear.
Before differential privacy can be robustly applied in practice in scenarios like that, mlr won't support it.
answered Nov 22 '18 at 18:15
Lars KotthoffLars Kotthoff
90.5k11158165
Short answer: mlr doesn't support that.
Long answer: My experience with differential privacy for machine learning is that in practice it doesn't work as well as advertised. In particular, to apply thresholdout you need a) copious amounts of data and b) the a priori probability that a given classifier will overfit on the given data -- something you can't easily determine in practice. While the paper you reference comes with example code that shows that thresholdout works in this particular case, but the amount of noise added in the code looks like it was determined on an ad-hoc basis; the relationship to the thresholdout algorithm described in the paper isn't clear.
Before differential privacy can be robustly applied in practice in scenarios like that, mlr won't support it.
answered Nov 22 '18 at 18:15
Lars KotthoffLars Kotthoff
90.5k11158165
answered Nov 22 '18 at 18:15
Lars KotthoffLars Kotthoff
90.5k11158165
answered Nov 22 '18 at 18:15
Lars KotthoffLars Kotthoff
90.5k11158165
answered Nov 22 '18 at 18:15
Lars KotthoffLars Kotthoff
90.5k11158165
90.5k11158165
Thank you for your answer, is there any alternative anyone tried to the method they describe that doesn't need the copious amount of data you describe?
– André Pinto
Nov 23 '18 at 19:06
Not that I'm aware of.
– Lars Kotthoff
Nov 23 '18 at 19:42
add a comment |
Thank you for your answer, is there any alternative anyone tried to the method they describe that doesn't need the copious amount of data you describe?
– André Pinto
Nov 23 '18 at 19:06
Not that I'm aware of.
– Lars Kotthoff
Nov 23 '18 at 19:42
Thank you for your answer, is there any alternative anyone tried to the method they describe that doesn't need the copious amount of data you describe?
– André Pinto
Nov 23 '18 at 19:06
Thank you for your answer, is there any alternative anyone tried to the method they describe that doesn't need the copious amount of data you describe?
– André Pinto
Nov 23 '18 at 19:06
Not that I'm aware of.
– Lars Kotthoff
Nov 23 '18 at 19:42
Not that I'm aware of.
– Lars Kotthoff
Nov 23 '18 at 19:42
add a comment |
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