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ML Work-Flow (Part 4) – Sanity Checks and Data Splitting

SANITY CHECK

We are now one step ahead of Feature Extraction and we extracted statistically important (covariate) representation of the given raw data. Just after Feature Extraction, first thing we need to do is to check the values of the new representation. In general, people are keen on avoiding this and regarding it as a waste of time. However, I believe this is a serious mistake. As I stated before, a single  NULL value, or skewed representation might cause a very big pain at the end and it can leave you in very hazy conditions.

Let’s start our discussion. I list here my Sanity Check steps;

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