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?
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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.
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$\begingroup$ 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? $\endgroup$ Mar 7, 2019 at 9:43
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$\begingroup$ @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/… $\endgroup$ Mar 7, 2019 at 11:23