What is the difference between normalized scaling and standardized scaling?

Normalization typically means rescales the values into a range of [0,1]. Standardization typically means rescales data to have a mean of 0 and a standard deviation of 1 (unit variance).

S.NO. Normalisation Standardisation
1. Minimum and maximum value of features are used for scaling Mean and standard deviation is used for scaling.
2. It is used when features are of different scales. It is used when we want to ensure zero mean and unit standard deviation.
3. Scales values between [0, 1] or [-1, 1]. It is not bounded to a certain range.
4. It is really affected by outliers. It is much less affected by outliers.
5. Scikit-Learn provides a transformer called MinMaxScaler for Normalization. Scikit-Learn provides a transformer called StandardScaler for standardization.
6. This transformation squishes the n-dimensional data into an n-dimensional unit hypercube. It translates the data to the mean vector of original data to the origin and squishes or expands.
7. It is useful when we don’t know about the distribution It is useful when the feature distribution is Normal or Gaussian.
8. It is a often called as Scaling Normalization It is a often called as Z-Score Normalization.