Python Machine Learning By Example - Second Edition

Scaling

Values of different features can differ by orders of magnitude. Sometimes, this may mean that the larger values dominate the smaller values. This depends on the algorithm we're using. For certain algorithms to work properly, we're required to scale the data.

There are following several common strategies that we can apply:

• Standardization removes the mean of a feature and divides by the standard deviation. If the feature values are normally distributed, we'll get a Gaussian, which is centered around zero with a variance of one.
• If the feature values aren't normally distributed, we can remove the median and divide by the interquartile range. The interquartile range is a range between the first and third quartile (or 25^th and 75^th