Skip to content

Vs (Normalization & Standardization)

Description

Normalization Standardization
Minimum and maximum value of features are used for scaling Mean and standard deviation is used for scaling.
It is used when features are of different scales. It is used when we want to ensure zero mean and unit standard deviation.
Scales values between [0, 1] or [-1, 1]. It is not bounded to a certain range.
It is really affected by outliers. It is much less affected by outliers.
Scikit-Learn provides a transformer called MinMaxScaler for Normalization. Scikit-Learn provides a transformer called StandardScaler for standardization.
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.
  • Min-max normalization guarantees all features will have the exact same scale but does not handle outliers well.
  • Z-score normalization handles outliers, but does not produce normalized data with the exact same scale.