How to deal with a potencially multiple categorical variable The Next CEO of Stack Overflow2019 Community Moderator ElectionHow to visualize (make plot) of regression output against categorical input variable?Dummy coding a column in R with multiple levelsAssociation Categorical VariablesBest approach for this unsupervised clustering problem with categorical data?Python: how to handle categorial values in dataset to build modelsPredicting with categorical dataClassifying variable types on a list of variablesShould I build a different model for each subsetWhat are the approaches to aggregate categorical variables?Dealing with multiple distinct-value categorical variables
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How to deal with a potencially multiple categorical variable
The Next CEO of Stack Overflow2019 Community Moderator ElectionHow to visualize (make plot) of regression output against categorical input variable?Dummy coding a column in R with multiple levelsAssociation Categorical VariablesBest approach for this unsupervised clustering problem with categorical data?Python: how to handle categorial values in dataset to build modelsPredicting with categorical dataClassifying variable types on a list of variablesShould I build a different model for each subsetWhat are the approaches to aggregate categorical variables?Dealing with multiple distinct-value categorical variables
$begingroup$
I'm build a model that has, as inputs, some categorical variables. I had already dealt with this sort of data before, and applied different techniques as creation of dummy variables and factor scoring. However, I have now a different type of problem which I can not see the obvious best answer to.
For each individual we can have multiple instances of this categorical variable $X$. When such cases happen on numerical variables I usually take the max/mean/min depending on context. I of course, one can use said context to build something similar here. However I'm curious about a general approach.
Assuming that for each object (row in our input matrix) we can have multiple entries of an categorical variable. Furthermore, assume that said variable can have many different values, and that for the context it can be relevant the combinations per row.
What would be a general approach to this variable?
feature-engineering categorical-data aggregation dummy-variables
asked Mar 25 at 9:22
Diogo SantosDiogo Santos
1112
$endgroup$
add a comment |
$begingroup$
I'm build a model that has, as inputs, some categorical variables. I had already dealt with this sort of data before, and applied different techniques as creation of dummy variables and factor scoring. However, I have now a different type of problem which I can not see the obvious best answer to.
For each individual we can have multiple instances of this categorical variable $X$. When such cases happen on numerical variables I usually take the max/mean/min depending on context. I of course, one can use said context to build something similar here. However I'm curious about a general approach.
Assuming that for each object (row in our input matrix) we can have multiple entries of an categorical variable. Furthermore, assume that said variable can have many different values, and that for the context it can be relevant the combinations per row.
What would be a general approach to this variable?
feature-engineering categorical-data aggregation dummy-variables
asked Mar 25 at 9:22
Diogo SantosDiogo Santos
1112
$endgroup$
add a comment |
$begingroup$
I'm build a model that has, as inputs, some categorical variables. I had already dealt with this sort of data before, and applied different techniques as creation of dummy variables and factor scoring. However, I have now a different type of problem which I can not see the obvious best answer to.
For each individual we can have multiple instances of this categorical variable $X$. When such cases happen on numerical variables I usually take the max/mean/min depending on context. I of course, one can use said context to build something similar here. However I'm curious about a general approach.
Assuming that for each object (row in our input matrix) we can have multiple entries of an categorical variable. Furthermore, assume that said variable can have many different values, and that for the context it can be relevant the combinations per row.
What would be a general approach to this variable?
feature-engineering categorical-data aggregation dummy-variables
asked Mar 25 at 9:22
Diogo SantosDiogo Santos
1112
$endgroup$
I'm build a model that has, as inputs, some categorical variables. I had already dealt with this sort of data before, and applied different techniques as creation of dummy variables and factor scoring. However, I have now a different type of problem which I can not see the obvious best answer to.
For each individual we can have multiple instances of this categorical variable $X$. When such cases happen on numerical variables I usually take the max/mean/min depending on context. I of course, one can use said context to build something similar here. However I'm curious about a general approach.
Assuming that for each object (row in our input matrix) we can have multiple entries of an categorical variable. Furthermore, assume that said variable can have many different values, and that for the context it can be relevant the combinations per row.
What would be a general approach to this variable?
feature-engineering categorical-data aggregation dummy-variables
feature-engineering categorical-data aggregation dummy-variables
asked Mar 25 at 9:22
Diogo SantosDiogo Santos
1112
asked Mar 25 at 9:22
Diogo SantosDiogo Santos
1112
asked Mar 25 at 9:22
Diogo SantosDiogo Santos
1112
asked Mar 25 at 9:22
Diogo SantosDiogo Santos
1112
asked Mar 25 at 9:22
Diogo SantosDiogo Santos
1112
1112
add a comment |
add a comment |
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