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I am working on a binary classification problem with 1000 rows and 20 variables.

I have variables like product_id, city, state, country, product family, product type, product segment etc etc..

As you can see that most of my variables are hierarchical variables. Meaning, if I know the city name, I can infer/populate other variables like state, country etc.

Same with product_id as well. If I know product_id info, I can get all info about product_family, product_segment, product type etc.

My questions are as follows

a) So, should I use only the granular level detail variables in my ML model and ignore other levels of same variable? because I guess it would be correlated

b) Any suggestion or tips on how can we handle this scenario in our model?

c) Should I drop this project because I only have very few granular level detail variables (and rest of it can be inferred based on them even without using AI)?

d) If I make a prediction, I believe the contribution of hierarchical variable is same. For ex: If variable city contributes to prediction by 10%, am I right that state, country all contribute the same 10% as well. (meaning all together contribute 10% to the outcome)

Can you help me with this please?v Looking forward to your inputs

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  • $\begingroup$ Is that Aurora, CO, or Aurora, IL? Or Aurora, Suriname, or Aurora, Victoria or… And no, if the city is 10% it doesn’t imply the state or country is 10%—if all the Auroras are Aurora, Santa Catarina, but the remaining 80% of the data isn’t Brazil… $\endgroup$ May 20, 2023 at 18:30

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Of course you could use only granular level variables but this would throw away a lot of information. The are different ways to leverage hierarchies. One way would be target encoding as described here Another possible solution would be the use of a hierarchical model. An example for a hierarchical regression problem is described here

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