Regression Analysis with Python by Luca Massaron & Alberto Boschetti

Author:Luca Massaron & Alberto Boschetti
Language: eng
Format: mobi
Publisher: Packt Publishing
Published: 2016-02-28T22:00:00+00:00

Numeric feature scaling

In Chapter 3, Multiple Regression in Action, inside the feature scaling section, we discussed how changing your original variables to a similar scale could help better interpret the resulting regression coefficients. Moreover, scaling is essential when using gradient descent-based algorithms because it facilitates quicker converging to a solution. For gradient descent, we will introduce other techniques that can only work using scaled features. However, apart for the technical requirements of certain algorithms, now our intention is to draw your attention to how feature scaling can be helpful when working with data that can sometimes be missing or faulty.

Missing or wrong data can happen not just during training but also during the production phase. Now, if a missing value is encountered, you have two design options to create a model sufficiently robust to cope with such a problem:

Actively deal with the missing values (there is a paragraph in this chapter devoted to this)

Passively deal with it and:Your system throws an error and everything goes down (and remains down till the problem is solved)

Your system ignores the missing data and computes the values that are not missing

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