How to identify one-to-many relation and discard one during feature selection Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar Manara 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsImportance of feature selection for boosting methodsDoes XGBoost handle multicollinearity by itself?Feature selection and classification accuracy relationFeature importance with high-cardinality categorical features for regression (numerical depdendent variable)Determining Important Atrributes with Feature SelectionFeature Importance and Partial Dependence plots seem to disagree?Correlation and feature selectionLSTM Feature selection processWhen to perform feature selection, how, and how does data affect choosing the predictive model?What's the difference between feature importance from Random Forest and Pearson correlation coefficient
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How to identify one-to-many relation and discard one during feature selection
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsImportance of feature selection for boosting methodsDoes XGBoost handle multicollinearity by itself?Feature selection and classification accuracy relationFeature importance with high-cardinality categorical features for regression (numerical depdendent variable)Determining Important Atrributes with Feature SelectionFeature Importance and Partial Dependence plots seem to disagree?Correlation and feature selectionLSTM Feature selection processWhen to perform feature selection, how, and how does data affect choosing the predictive model?What's the difference between feature importance from Random Forest and Pearson correlation coefficient
$begingroup$
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 7 mins 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 7 mins 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 7 mins 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 7 mins ago
viral kapadiaviral kapadia
111
asked 7 mins ago
viral kapadiaviral kapadia
111
asked 7 mins ago
viral kapadiaviral kapadia
111
asked 7 mins ago
viral kapadiaviral kapadia
111
asked 7 mins ago
viral kapadiaviral kapadia
111
111
add a comment |
add a comment |
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