Of the two best known techniques for feature scaling in Machine Learning:

• Normalizing a feature to a $[0, 1]$ range, through $x - x_{min} \over x_{max} - x_{min}$

or

• Standardizing the feature (also referred to as z-score), through $x - μ \over σ$, where $μ$ is the mean and $σ$ is the standard deviation.

Is there any reason to prefer one over the other? Does any one outperform the other when used with certain algorithms?

## closed as too broad by Michael Chernick, mdewey, mkt, Peter Flom♦Sep 15 '18 at 12:05

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

• It depends on the objective. – user2974951 Sep 14 '18 at 12:32
• @user2974951 Is there any case where one is preferred over the other? Or does one have any useful properties compared to the other? – kfn95 Sep 14 '18 at 12:35
• Your comment makes me believe this is a possible duplicate of Is it a good practice to always scale/normalize data for machine learning? – Frans Rodenburg Sep 14 '18 at 12:53
• @FransRodenburg The author asks which feature scaling to perform, while the question you referenced is about whether or not to use feature scaling. I think it's a valid question. – John Doe Sep 14 '18 at 14:17
• @JohnDoe I realise the difference in titles, but if you read the accepted answer you'll see that it is more similar than it might seem at first. – Frans Rodenburg Sep 14 '18 at 14:19