How to choose an optimal threshold for binary discretization
$begingroup$
We know that we usually do discretizations to continuous features to remove extra information and unwanted regularities, which makes the model robust and well-predicted.
But I am wondering except based on the context of feature how to choose an optimal threshold for data discretization, binary or multi.
machine-learning data feature-engineering features
asked 4 mins ago
FreAk PointFreAk Point
303
$endgroup$
add a comment |
$begingroup$
We know that we usually do discretizations to continuous features to remove extra information and unwanted regularities, which makes the model robust and well-predicted.
But I am wondering except based on the context of feature how to choose an optimal threshold for data discretization, binary or multi.
machine-learning data feature-engineering features
asked 4 mins ago
FreAk PointFreAk Point
303
$endgroup$
add a comment |
$begingroup$
We know that we usually do discretizations to continuous features to remove extra information and unwanted regularities, which makes the model robust and well-predicted.
But I am wondering except based on the context of feature how to choose an optimal threshold for data discretization, binary or multi.
machine-learning data feature-engineering features
asked 4 mins ago
FreAk PointFreAk Point
303
$endgroup$
We know that we usually do discretizations to continuous features to remove extra information and unwanted regularities, which makes the model robust and well-predicted.
But I am wondering except based on the context of feature how to choose an optimal threshold for data discretization, binary or multi.
machine-learning data feature-engineering features
machine-learning data feature-engineering features
asked 4 mins ago
FreAk PointFreAk Point
303
asked 4 mins ago
FreAk PointFreAk Point
303
asked 4 mins ago
FreAk PointFreAk Point
303
asked 4 mins ago
FreAk PointFreAk Point
303
asked 4 mins ago
FreAk PointFreAk Point
303
303
add a comment |
add a comment |
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