Machine learning model to predict the best candidate
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I have several training examples, each of which consists of a set of candidates and a label that tells which one of those candidates is the best in that set.
The number of candidates in every set may be different. The set of candidates is unordered. In my current problem, each candidate is represented by a fixed length feature vector. However in future, the number of features describing each candidate may also be different for every candidate.
I would like to build a machine learning model that can predict the best candidate from any given set. What could be a good architecture for such a model?
One approach I tried was a simple MLP that takes one candidate as input and outputs whether or not the candidate is best. But since this MLP wouldn't know which set the candidate belongs to, it fails in situations where a candidate is the best in one set but the same candidate is not the best in another set.
machine-learning neural-network prediction machine-learning-model
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MakMak
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$begingroup$
I have several training examples, each of which consists of a set of candidates and a label that tells which one of those candidates is the best in that set.
The number of candidates in every set may be different. The set of candidates is unordered. In my current problem, each candidate is represented by a fixed length feature vector. However in future, the number of features describing each candidate may also be different for every candidate.
I would like to build a machine learning model that can predict the best candidate from any given set. What could be a good architecture for such a model?
One approach I tried was a simple MLP that takes one candidate as input and outputs whether or not the candidate is best. But since this MLP wouldn't know which set the candidate belongs to, it fails in situations where a candidate is the best in one set but the same candidate is not the best in another set.
machine-learning neural-network prediction machine-learning-model
asked 5 mins ago
MakMak
11
New contributor
Mak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have several training examples, each of which consists of a set of candidates and a label that tells which one of those candidates is the best in that set.
The number of candidates in every set may be different. The set of candidates is unordered. In my current problem, each candidate is represented by a fixed length feature vector. However in future, the number of features describing each candidate may also be different for every candidate.
I would like to build a machine learning model that can predict the best candidate from any given set. What could be a good architecture for such a model?
One approach I tried was a simple MLP that takes one candidate as input and outputs whether or not the candidate is best. But since this MLP wouldn't know which set the candidate belongs to, it fails in situations where a candidate is the best in one set but the same candidate is not the best in another set.
machine-learning neural-network prediction machine-learning-model
asked 5 mins ago
MakMak
11
New contributor
Mak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have several training examples, each of which consists of a set of candidates and a label that tells which one of those candidates is the best in that set.
The number of candidates in every set may be different. The set of candidates is unordered. In my current problem, each candidate is represented by a fixed length feature vector. However in future, the number of features describing each candidate may also be different for every candidate.
I would like to build a machine learning model that can predict the best candidate from any given set. What could be a good architecture for such a model?
One approach I tried was a simple MLP that takes one candidate as input and outputs whether or not the candidate is best. But since this MLP wouldn't know which set the candidate belongs to, it fails in situations where a candidate is the best in one set but the same candidate is not the best in another set.
machine-learning neural-network prediction machine-learning-model
machine-learning neural-network prediction machine-learning-model
asked 5 mins ago
MakMak
11
New contributor
Mak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
MakMak
11
New contributor
Mak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
MakMak
11
New contributor
Mak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
MakMak
11
asked 5 mins ago
MakMak
11
11
New contributor
Mak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Mak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Mak is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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