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. |
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