Chapter 16
Detecting Outliers in Data
IN THIS CHAPTER
Understanding what is an outlier
Distinguishing between extreme values and novelties
Using simple statistics for catching outliers
Finding out most tricky outliers by advanced techniques
Errors happen when you least expect, and that’s also true in regard to your data. In addition, data errors are difficult to spot, especially when your dataset contains many variables of different types and scale. Data errors can take a number of forms. For example, the values may be systematically missing on certain variables, erroneous numbers could appear here and there, and the data could include outliers.
In this chapter, you not only will learn what is an outlier and why it differs from a novelty value, but you will find techniques to detect and replace those examples that deviate from the data distribution you want to be represented by your machine learning models.
You don’t have to type the source code for this chapter manually; using the ...
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