EDA for analysis of nominal variable with high cardinality The 2019 Stack Overflow Developer Survey Results Are InWhat are the approaches to aggregate categorical variables?Kmeans on mixed dataset with high level for categClustering for mixed numeric and nominal discrete dataHow to deal with categorical feature of very high cardinality?Feature importance with high-cardinality categorical features for regression (numerical depdendent variable)Categorical, nominal or continuous variable?Is there a name for a scale which mixes ordinal and nominal?Nominal categorical variable with two levels: Label Encoding or One Hot encoding?How to deal with Nominal categorical with label encoding?Dummy variable for Categorical valuesResponse variable is nominal.
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EDA for analysis of nominal variable with high cardinality
The 2019 Stack Overflow Developer Survey Results Are InWhat are the approaches to aggregate categorical variables?Kmeans on mixed dataset with high level for categClustering for mixed numeric and nominal discrete dataHow to deal with categorical feature of very high cardinality?Feature importance with high-cardinality categorical features for regression (numerical depdendent variable)Categorical, nominal or continuous variable?Is there a name for a scale which mixes ordinal and nominal?Nominal categorical variable with two levels: Label Encoding or One Hot encoding?How to deal with Nominal categorical with label encoding?Dummy variable for Categorical valuesResponse variable is nominal.
$begingroup$
I have a nominal variable (car model) with very high cardinality (~8500 labels) and I would like to analyse its relation with a binary target variable. While I can create logical groups and compare the distribution of target variable for each of the groups, can anyone suggest if there are any superior techniques/visualization tools for this type of analysis?
categorical-data data-analysis
asked Mar 1 at 6:06
Rohit GavvalRohit Gavval
658
$endgroup$
add a comment |
$begingroup$
I have a nominal variable (car model) with very high cardinality (~8500 labels) and I would like to analyse its relation with a binary target variable. While I can create logical groups and compare the distribution of target variable for each of the groups, can anyone suggest if there are any superior techniques/visualization tools for this type of analysis?
categorical-data data-analysis
asked Mar 1 at 6:06
Rohit GavvalRohit Gavval
658
$endgroup$
add a comment |
$begingroup$
I have a nominal variable (car model) with very high cardinality (~8500 labels) and I would like to analyse its relation with a binary target variable. While I can create logical groups and compare the distribution of target variable for each of the groups, can anyone suggest if there are any superior techniques/visualization tools for this type of analysis?
categorical-data data-analysis
asked Mar 1 at 6:06
Rohit GavvalRohit Gavval
658
$endgroup$
I have a nominal variable (car model) with very high cardinality (~8500 labels) and I would like to analyse its relation with a binary target variable. While I can create logical groups and compare the distribution of target variable for each of the groups, can anyone suggest if there are any superior techniques/visualization tools for this type of analysis?
categorical-data data-analysis
categorical-data data-analysis
asked Mar 1 at 6:06
Rohit GavvalRohit Gavval
658
asked Mar 1 at 6:06
Rohit GavvalRohit Gavval
658
asked Mar 1 at 6:06
Rohit GavvalRohit Gavval
658
asked Mar 1 at 6:06
Rohit GavvalRohit Gavval
658
asked Mar 1 at 6:06
Rohit GavvalRohit Gavval
658
658
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
You can calculate mean target for each categorical variable and compare its values.
In pandas this can be done easily: df.groupby('categorical_feature').target.mean()
Then you can make a histogram to compare the approach. I also, seaborn has a catplot, where it do the same as above in a bar plot format, showing mean value for target variable based on each categorical one.
answered Mar 1 at 13:09
Victor OliveiraVictor Oliveira
3657
$endgroup$
$begingroup$
My target variable is dichotomous. So taking the mean is not an option. May be I can take count, but the real problem is that I have around 8000 levels in one categorical attribute. How can I study that?
$endgroup$
– Rohit Gavval
Mar 7 at 9:43
$begingroup$
@RohitGavval, if you have a binary variable, you can calculate mean. It will be something like 0.333, 0.67, that is the point. Look at my answer to this question where I put the links with more explanation for the mentioned methods: datascience.stackexchange.com/questions/46780/…
$endgroup$
– Victor Oliveira
Mar 7 at 11:23
add a comment |
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1 Answer
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1 Answer
1
active
oldest
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active
oldest
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active
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votes
$begingroup$
You can calculate mean target for each categorical variable and compare its values.
In pandas this can be done easily: df.groupby('categorical_feature').target.mean()
Then you can make a histogram to compare the approach. I also, seaborn has a catplot, where it do the same as above in a bar plot format, showing mean value for target variable based on each categorical one.
answered Mar 1 at 13:09
Victor OliveiraVictor Oliveira
3657
$endgroup$
$begingroup$
My target variable is dichotomous. So taking the mean is not an option. May be I can take count, but the real problem is that I have around 8000 levels in one categorical attribute. How can I study that?
$endgroup$
– Rohit Gavval
Mar 7 at 9:43
$begingroup$
@RohitGavval, if you have a binary variable, you can calculate mean. It will be something like 0.333, 0.67, that is the point. Look at my answer to this question where I put the links with more explanation for the mentioned methods: datascience.stackexchange.com/questions/46780/…
$endgroup$
– Victor Oliveira
Mar 7 at 11:23
add a comment |
$begingroup$
You can calculate mean target for each categorical variable and compare its values.
In pandas this can be done easily: df.groupby('categorical_feature').target.mean()
Then you can make a histogram to compare the approach. I also, seaborn has a catplot, where it do the same as above in a bar plot format, showing mean value for target variable based on each categorical one.
answered Mar 1 at 13:09
Victor OliveiraVictor Oliveira
3657
$endgroup$
$begingroup$
My target variable is dichotomous. So taking the mean is not an option. May be I can take count, but the real problem is that I have around 8000 levels in one categorical attribute. How can I study that?
$endgroup$
– Rohit Gavval
Mar 7 at 9:43
$begingroup$
@RohitGavval, if you have a binary variable, you can calculate mean. It will be something like 0.333, 0.67, that is the point. Look at my answer to this question where I put the links with more explanation for the mentioned methods: datascience.stackexchange.com/questions/46780/…
$endgroup$
– Victor Oliveira
Mar 7 at 11:23
add a comment |
$begingroup$
You can calculate mean target for each categorical variable and compare its values.
In pandas this can be done easily: df.groupby('categorical_feature').target.mean()
Then you can make a histogram to compare the approach. I also, seaborn has a catplot, where it do the same as above in a bar plot format, showing mean value for target variable based on each categorical one.
answered Mar 1 at 13:09
Victor OliveiraVictor Oliveira
3657
$endgroup$
You can calculate mean target for each categorical variable and compare its values.
In pandas this can be done easily: df.groupby('categorical_feature').target.mean()
Then you can make a histogram to compare the approach. I also, seaborn has a catplot, where it do the same as above in a bar plot format, showing mean value for target variable based on each categorical one.
answered Mar 1 at 13:09
Victor OliveiraVictor Oliveira
3657
answered Mar 1 at 13:09
Victor OliveiraVictor Oliveira
3657
answered Mar 1 at 13:09
Victor OliveiraVictor Oliveira
3657
answered Mar 1 at 13:09
Victor OliveiraVictor Oliveira
3657
3657
$begingroup$
My target variable is dichotomous. So taking the mean is not an option. May be I can take count, but the real problem is that I have around 8000 levels in one categorical attribute. How can I study that?
$endgroup$
– Rohit Gavval
Mar 7 at 9:43
$begingroup$
@RohitGavval, if you have a binary variable, you can calculate mean. It will be something like 0.333, 0.67, that is the point. Look at my answer to this question where I put the links with more explanation for the mentioned methods: datascience.stackexchange.com/questions/46780/…
$endgroup$
– Victor Oliveira
Mar 7 at 11:23
add a comment |
$begingroup$
My target variable is dichotomous. So taking the mean is not an option. May be I can take count, but the real problem is that I have around 8000 levels in one categorical attribute. How can I study that?
$endgroup$
– Rohit Gavval
Mar 7 at 9:43
$begingroup$
@RohitGavval, if you have a binary variable, you can calculate mean. It will be something like 0.333, 0.67, that is the point. Look at my answer to this question where I put the links with more explanation for the mentioned methods: datascience.stackexchange.com/questions/46780/…
$endgroup$
– Victor Oliveira
Mar 7 at 11:23
$begingroup$
My target variable is dichotomous. So taking the mean is not an option. May be I can take count, but the real problem is that I have around 8000 levels in one categorical attribute. How can I study that?
$endgroup$
– Rohit Gavval
Mar 7 at 9:43
$begingroup$
My target variable is dichotomous. So taking the mean is not an option. May be I can take count, but the real problem is that I have around 8000 levels in one categorical attribute. How can I study that?
$endgroup$
– Rohit Gavval
Mar 7 at 9:43
$begingroup$
@RohitGavval, if you have a binary variable, you can calculate mean. It will be something like 0.333, 0.67, that is the point. Look at my answer to this question where I put the links with more explanation for the mentioned methods: datascience.stackexchange.com/questions/46780/…
$endgroup$
– Victor Oliveira
Mar 7 at 11:23
$begingroup$
@RohitGavval, if you have a binary variable, you can calculate mean. It will be something like 0.333, 0.67, that is the point. Look at my answer to this question where I put the links with more explanation for the mentioned methods: datascience.stackexchange.com/questions/46780/…
$endgroup$
– Victor Oliveira
Mar 7 at 11:23
add a comment |
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