How to identify one-to-many relation and discard one during feature selection
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My data has many features out of which two features have one-to-many relation something like state and country. Now I want to do feature selection to identify key independent features for given dependent feature. I use random tree forest feature importance. While its reasonabally good and gave more Weightage to state (40%), it also gave 10% weightage to Country. Knowing the data I expect it to consider Country as redundant as State is more granular. What is the best way to identify such redundancy (Country in this case) while doing feature selection on data domain one is not so familiar with.
feature-selection random-forest
asked 45 secs ago
viral kapadiaviral kapadia
111
$endgroup$
add a comment |
$begingroup$
My data has many features out of which two features have one-to-many relation something like state and country. Now I want to do feature selection to identify key independent features for given dependent feature. I use random tree forest feature importance. While its reasonabally good and gave more Weightage to state (40%), it also gave 10% weightage to Country. Knowing the data I expect it to consider Country as redundant as State is more granular. What is the best way to identify such redundancy (Country in this case) while doing feature selection on data domain one is not so familiar with.
feature-selection random-forest
asked 45 secs ago
viral kapadiaviral kapadia
111
$endgroup$
add a comment |
$begingroup$
My data has many features out of which two features have one-to-many relation something like state and country. Now I want to do feature selection to identify key independent features for given dependent feature. I use random tree forest feature importance. While its reasonabally good and gave more Weightage to state (40%), it also gave 10% weightage to Country. Knowing the data I expect it to consider Country as redundant as State is more granular. What is the best way to identify such redundancy (Country in this case) while doing feature selection on data domain one is not so familiar with.
feature-selection random-forest
asked 45 secs ago
viral kapadiaviral kapadia
111
$endgroup$
My data has many features out of which two features have one-to-many relation something like state and country. Now I want to do feature selection to identify key independent features for given dependent feature. I use random tree forest feature importance. While its reasonabally good and gave more Weightage to state (40%), it also gave 10% weightage to Country. Knowing the data I expect it to consider Country as redundant as State is more granular. What is the best way to identify such redundancy (Country in this case) while doing feature selection on data domain one is not so familiar with.
feature-selection random-forest
feature-selection random-forest
asked 45 secs ago
viral kapadiaviral kapadia
111
asked 45 secs ago
viral kapadiaviral kapadia
111
asked 45 secs ago
viral kapadiaviral kapadia
111
asked 45 secs ago
viral kapadiaviral kapadia
111
asked 45 secs ago
viral kapadiaviral kapadia
111
111
add a comment |
add a comment |
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