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Is it advisable to use output from a ML model as a feature in another ML model?
Use matrix feature for machine learning or cluster analysisSupervised learning for audio files with uncertain labelsHow to scale up a model in a training dataset to cover all aspects of training dataHow to deal with feature and target variable at different granularities?A scenario of developing machine learning modelfeature engineering for auto encoder anomaly detectionHow to handle Feature changes in a model deployed?Making a model to predict the error of another modelWhy feature transformation is needed in machine learning & statistics? Doesn't it affect the “interaction” between features?Predicting Transformation
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Can I use the probability score generated from a Machine Learning model as a feature in another model? For example, say we have a model which generates the probability of an ad being bad. Lets call it badness_score. I am working on building another model which will predict the probability of an advertiser being a fraudster. Can I use badness_score as a feature while building this model ?
Are there any caveats to this approach?
machine-learning feature-engineering
edited Apr 24 at 14:20
gung♦
110k34269540
asked Apr 24 at 13:06
aditya bhataditya bhat
161
New contributor
aditya bhat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
Can I use the probability score generated from a Machine Learning model as a feature in another model? For example, say we have a model which generates the probability of an ad being bad. Lets call it badness_score. I am working on building another model which will predict the probability of an advertiser being a fraudster. Can I use badness_score as a feature while building this model ?
Are there any caveats to this approach?
machine-learning feature-engineering
edited Apr 24 at 14:20
gung♦
110k34269540
asked Apr 24 at 13:06
aditya bhataditya bhat
161
New contributor
aditya bhat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
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You can use it as a feature, sure. However I'd imagine the utility of this feature being strongly connected to the quality of the first model - if it captures the underlying pattern poorly, it's probably not beneficial to the second model.
$endgroup$
– Rickyfox
Apr 24 at 13:57
add a comment |
$begingroup$
Can I use the probability score generated from a Machine Learning model as a feature in another model? For example, say we have a model which generates the probability of an ad being bad. Lets call it badness_score. I am working on building another model which will predict the probability of an advertiser being a fraudster. Can I use badness_score as a feature while building this model ?
Are there any caveats to this approach?
machine-learning feature-engineering
edited Apr 24 at 14:20
gung♦
110k34269540
asked Apr 24 at 13:06
aditya bhataditya bhat
161
New contributor
aditya bhat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Can I use the probability score generated from a Machine Learning model as a feature in another model? For example, say we have a model which generates the probability of an ad being bad. Lets call it badness_score. I am working on building another model which will predict the probability of an advertiser being a fraudster. Can I use badness_score as a feature while building this model ?
Are there any caveats to this approach?
machine-learning feature-engineering
machine-learning feature-engineering
edited Apr 24 at 14:20
gung♦
110k34269540
asked Apr 24 at 13:06
aditya bhataditya bhat
161
New contributor
aditya bhat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited Apr 24 at 14:20
gung♦
110k34269540
asked Apr 24 at 13:06
aditya bhataditya bhat
161
New contributor
aditya bhat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited Apr 24 at 14:20
gung♦
110k34269540
edited Apr 24 at 14:20
gung♦
110k34269540
edited Apr 24 at 14:20
gung♦
110k34269540
110k34269540
asked Apr 24 at 13:06
aditya bhataditya bhat
161
New contributor
aditya bhat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Apr 24 at 13:06
aditya bhataditya bhat
161
asked Apr 24 at 13:06
aditya bhataditya bhat
161
161
New contributor
aditya bhat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
aditya bhat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
aditya bhat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
You can use it as a feature, sure. However I'd imagine the utility of this feature being strongly connected to the quality of the first model - if it captures the underlying pattern poorly, it's probably not beneficial to the second model.
$endgroup$
– Rickyfox
Apr 24 at 13:57
add a comment |
$begingroup$
You can use it as a feature, sure. However I'd imagine the utility of this feature being strongly connected to the quality of the first model - if it captures the underlying pattern poorly, it's probably not beneficial to the second model.
$endgroup$
– Rickyfox
Apr 24 at 13:57
$begingroup$
You can use it as a feature, sure. However I'd imagine the utility of this feature being strongly connected to the quality of the first model - if it captures the underlying pattern poorly, it's probably not beneficial to the second model.
$endgroup$
– Rickyfox
Apr 24 at 13:57
$begingroup$
You can use it as a feature, sure. However I'd imagine the utility of this feature being strongly connected to the quality of the first model - if it captures the underlying pattern poorly, it's probably not beneficial to the second model.
$endgroup$
– Rickyfox
Apr 24 at 13:57
add a comment |
1 Answer
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It's perfectly OK, and also widely used. Different models can explain different perspectives of data and stacking them in front of each other, and using outputs/predictions produced by previous layers enables even moderately simple final layer algorithms to perform much better compared to on their own, because they use the cumulative knowledge learned via other algorithms. This is somewhat analogous to adding layers to neural networks.
answered Apr 24 at 13:13
gunesgunes
8,2911418
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1 Answer
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1 Answer
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$begingroup$
It's perfectly OK, and also widely used. Different models can explain different perspectives of data and stacking them in front of each other, and using outputs/predictions produced by previous layers enables even moderately simple final layer algorithms to perform much better compared to on their own, because they use the cumulative knowledge learned via other algorithms. This is somewhat analogous to adding layers to neural networks.
answered Apr 24 at 13:13
gunesgunes
8,2911418
$endgroup$
add a comment |
$begingroup$
It's perfectly OK, and also widely used. Different models can explain different perspectives of data and stacking them in front of each other, and using outputs/predictions produced by previous layers enables even moderately simple final layer algorithms to perform much better compared to on their own, because they use the cumulative knowledge learned via other algorithms. This is somewhat analogous to adding layers to neural networks.
answered Apr 24 at 13:13
gunesgunes
8,2911418
$endgroup$
add a comment |
$begingroup$
It's perfectly OK, and also widely used. Different models can explain different perspectives of data and stacking them in front of each other, and using outputs/predictions produced by previous layers enables even moderately simple final layer algorithms to perform much better compared to on their own, because they use the cumulative knowledge learned via other algorithms. This is somewhat analogous to adding layers to neural networks.
answered Apr 24 at 13:13
gunesgunes
8,2911418
$endgroup$
It's perfectly OK, and also widely used. Different models can explain different perspectives of data and stacking them in front of each other, and using outputs/predictions produced by previous layers enables even moderately simple final layer algorithms to perform much better compared to on their own, because they use the cumulative knowledge learned via other algorithms. This is somewhat analogous to adding layers to neural networks.
answered Apr 24 at 13:13
gunesgunes
8,2911418
answered Apr 24 at 13:13
gunesgunes
8,2911418
answered Apr 24 at 13:13
gunesgunes
8,2911418
answered Apr 24 at 13:13
gunesgunes
8,2911418
8,2911418
add a comment |
add a comment |
aditya bhat is a new contributor. Be nice, and check out our Code of Conduct.
aditya bhat is a new contributor. Be nice, and check out our Code of Conduct.
aditya bhat is a new contributor. Be nice, and check out our Code of Conduct.
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You can use it as a feature, sure. However I'd imagine the utility of this feature being strongly connected to the quality of the first model - if it captures the underlying pattern poorly, it's probably not beneficial to the second model.
$endgroup$
– Rickyfox
Apr 24 at 13:57