0
$\begingroup$

I have data, that describes distance from given location to nearest object (e.g. school, shop etc). Because of performance reasons I couldn't scrape the data about objects, that are futher away than 2.5km. Now I have quite a lot of missing values in different variables).

My question is: What is the best way to fill such a missing data?

I considered such a solutions: - Binning data into few categories - one of them would be "more then 2.5km", - Replacing missing data with maximum value of each variable, - Replacing missing data with value greater then maximum.

Disadvantage of the first solution is, that I lose a lot of information. The other solutions disturb the distribution of the variables.

I use those variables in xgboost model both regression and classification.

Could you please give me some advice, which solution of these would be better. Or maybe I am missing something and there is any other better solution? Scraping it again with greater radius is not a solution, because it will leave missing values again.

$\endgroup$

Your Answer

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

Browse other questions tagged or ask your own question.