2
$\begingroup$

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?

$\endgroup$
1
$\begingroup$

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.

| improve this answer | |
$\endgroup$
  • $\begingroup$ 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 '19 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 '19 at 11:23

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.