xgboost feature importance of categorical variable
I am using XGBClassifier to train in python and there are a handful of categorical variables in my training dataset. Originally, I planed to convert each of them into a few dummies before I throw in my data, but then the feature importance will be calculated for each dummy, not the original categorical ones. Since I also need to order all of my original variables (including numerical + categorical) by importance, I am wondering how to get importance of my original variables? Is it simply adding up?
python xgboost categorical-data
asked Nov 15 '18 at 20:21
pingboingpingboing
84
add a comment |
I am using XGBClassifier to train in python and there are a handful of categorical variables in my training dataset. Originally, I planed to convert each of them into a few dummies before I throw in my data, but then the feature importance will be calculated for each dummy, not the original categorical ones. Since I also need to order all of my original variables (including numerical + categorical) by importance, I am wondering how to get importance of my original variables? Is it simply adding up?
python xgboost categorical-data
asked Nov 15 '18 at 20:21
pingboingpingboing
84
add a comment |
I am using XGBClassifier to train in python and there are a handful of categorical variables in my training dataset. Originally, I planed to convert each of them into a few dummies before I throw in my data, but then the feature importance will be calculated for each dummy, not the original categorical ones. Since I also need to order all of my original variables (including numerical + categorical) by importance, I am wondering how to get importance of my original variables? Is it simply adding up?
python xgboost categorical-data
asked Nov 15 '18 at 20:21
pingboingpingboing
84
I am using XGBClassifier to train in python and there are a handful of categorical variables in my training dataset. Originally, I planed to convert each of them into a few dummies before I throw in my data, but then the feature importance will be calculated for each dummy, not the original categorical ones. Since I also need to order all of my original variables (including numerical + categorical) by importance, I am wondering how to get importance of my original variables? Is it simply adding up?
python xgboost categorical-data
python xgboost categorical-data
asked Nov 15 '18 at 20:21
pingboingpingboing
84
asked Nov 15 '18 at 20:21
pingboingpingboing
84
asked Nov 15 '18 at 20:21
pingboingpingboing
84
asked Nov 15 '18 at 20:21
pingboingpingboing
84
asked Nov 15 '18 at 20:21
pingboingpingboing
84
84
add a comment |
add a comment |
1 Answer
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You could probably get by with summing the individual categories' importances into their original, parent category. But, unless these features are high-cardinality, my two cents would be to report them individually. I tend to err on the side of being more explicit with reporting model performance/importance measures.
answered Nov 15 '18 at 20:24
blacksiteblacksite
5,64241747
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1 Answer
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1 Answer
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active
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You could probably get by with summing the individual categories' importances into their original, parent category. But, unless these features are high-cardinality, my two cents would be to report them individually. I tend to err on the side of being more explicit with reporting model performance/importance measures.
answered Nov 15 '18 at 20:24
blacksiteblacksite
5,64241747
add a comment |
You could probably get by with summing the individual categories' importances into their original, parent category. But, unless these features are high-cardinality, my two cents would be to report them individually. I tend to err on the side of being more explicit with reporting model performance/importance measures.
answered Nov 15 '18 at 20:24
blacksiteblacksite
5,64241747
add a comment |
You could probably get by with summing the individual categories' importances into their original, parent category. But, unless these features are high-cardinality, my two cents would be to report them individually. I tend to err on the side of being more explicit with reporting model performance/importance measures.
answered Nov 15 '18 at 20:24
blacksiteblacksite
5,64241747
You could probably get by with summing the individual categories' importances into their original, parent category. But, unless these features are high-cardinality, my two cents would be to report them individually. I tend to err on the side of being more explicit with reporting model performance/importance measures.
answered Nov 15 '18 at 20:24
blacksiteblacksite
5,64241747
answered Nov 15 '18 at 20:24
blacksiteblacksite
5,64241747
answered Nov 15 '18 at 20:24
blacksiteblacksite
5,64241747
answered Nov 15 '18 at 20:24
blacksiteblacksite
5,64241747
5,64241747
add a comment |
add a comment |
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