The website is in Maintenance mode. We are in the process of adding more features.
Any new bookmarks, comments, or user profiles made during this time will not be saved.

Machine Learning Resources

What does Centering and Scaling mean? What is the individual effect of each of those?

Bookmark this question

When preparing our ‘Training Data’, two basic pre-processing techniques, applicable to Numerical Features, are ‘Centering’ and ‘Scaling’. These are usually applied together and maybe necessary to transform raw numerical data into a format that is suitable for the algorithms of choice.

Centering our data means that we alter the position of its mean, by applying a constant to each data point, shifting the response curve up/down. The objective, in Standardization, is to achieve a mean that is equal to zero. By only ‘Centering’ the data variance / relative magnitudes of the data remains the same, as does the unit, only the mean is altered.

Scaling our data means that it is transformed so as to fit within a single specific range, it is a technique that is useful to ensure that different Features can be compared without the risk of overshadowing others that have a different range. It is common to scale Features, as in Standardization, so that they have a Standard Deviation of 1. However ‘Scaling’ a Features min & max values between 0 & 1 (or -1 & 1 if negative values are present) is performed during ‘‘Min-Max Scaling’

Leave your Comments and Suggestions below:

Please Login or Sign Up to leave a comment

Partner Ad  

Find out all the ways
that you can

Explore Questions by Topics