To use UserId as a feature or not to train different ML Model
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Currently, I am working on electric load forecasting. Should I use userId as a feature to train one ML model or train as many models as a number of unique userID in the system (In this case automatically no need for userId as a feature)?
I think both are fine. In the first case as many numbers of dimension will be added as a unique feature this is fine too but I am not sure.
Machine learning model which I am currently looking is Random Forest, SVM etc. can anybody tell which approach is best.
machine-learning regression feature-selection decision-trees forecast
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$begingroup$
Currently, I am working on electric load forecasting. Should I use userId as a feature to train one ML model or train as many models as a number of unique userID in the system (In this case automatically no need for userId as a feature)?
I think both are fine. In the first case as many numbers of dimension will be added as a unique feature this is fine too but I am not sure.
Machine learning model which I am currently looking is Random Forest, SVM etc. can anybody tell which approach is best.
machine-learning regression feature-selection decision-trees forecast
New contributor
$endgroup$
add a comment |
$begingroup$
Currently, I am working on electric load forecasting. Should I use userId as a feature to train one ML model or train as many models as a number of unique userID in the system (In this case automatically no need for userId as a feature)?
I think both are fine. In the first case as many numbers of dimension will be added as a unique feature this is fine too but I am not sure.
Machine learning model which I am currently looking is Random Forest, SVM etc. can anybody tell which approach is best.
machine-learning regression feature-selection decision-trees forecast
New contributor
$endgroup$
Currently, I am working on electric load forecasting. Should I use userId as a feature to train one ML model or train as many models as a number of unique userID in the system (In this case automatically no need for userId as a feature)?
I think both are fine. In the first case as many numbers of dimension will be added as a unique feature this is fine too but I am not sure.
Machine learning model which I am currently looking is Random Forest, SVM etc. can anybody tell which approach is best.
machine-learning regression feature-selection decision-trees forecast
machine-learning regression feature-selection decision-trees forecast
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New contributor
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asked 9 mins ago
Ayush MishraAyush Mishra
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Ayush Mishra is a new contributor. Be nice, and check out our Code of Conduct.
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