When to use Arbitrary or end-of-distribution value imputation
Channel: Data Scholar
Date: 2022-11-16

Let's say you have a dataset with several numerical features, and some of the features have missing values. The first thing that you would do is figure out why the values are missing, are they missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR)? Depending on which category it belongs to, the handling would be different.

I have covered Complete Case Analysis in another video, you can watch it here: https://youtu.be/hnuXBN4JCTs

If you have a lot of missing data, dropping missing values would not be a good choice. This is where you have to think of another way to handle missing values, and if the feature is numerical, the strategy is different than if it is categorical. Then comes the question of whether univariate or multivariate imputing would work.

For univariate imputing of numerical features, you have a choice between one of the following strategies:

1- Mean/Median Imputation  https://youtu.be/TU_5SOUFyMs
2- Imputing with an arbitrary value (Covered in this video)
3- End of distribution Imputation (Covered in this video)
4- Random Value Imputation (will be covered in next video)
5- Automatic Selection of Best Imputation Parameter (will be covered in next video)

Which of these strategies should I employ now? 

I have broken down the mathematics of several required topics and have discussed the pros, cons, when to use, when not to use of every technique. Finally, have shown every thing in  #python code
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