Due to various reasons, datasets in the real world are rarely completely clean and often contain missing or corrupted values. They are usually presented as blanks, Null, -1, 999999, unknown, or any other placeholder. Samples with missing data not only provide incomplete predictive information, but also confuse the machine learning model as it can not tell whether -1 or unknown holds a meaning. It is important to pinpoint and deal with missing data in order to avoid jeopardizing the performance of models in later stages.
Here are three basic strategies that we can use to tackle the missing data issue:
- Discarding samples containing any missing value
- Discarding fields containing missing values in ...
