Python Machine Learning By Example

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 are using. For certain algorithms to work properly we are required to scale the data. There are 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 will get a Gaussian, which is centered around zero with a variance of one.
• If the feature values are not 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 25th and 75th percentile). ...