Normalization & Scaling

1. A comparison of normalization / scaling techniques in sklearn

2. Another great explanation on sklearn and (general) scaling - normal, min max, etc..

3. Normalization\standardize features

• data has varying scales

• Normalize between range 0 to 1.

  • When the algorithm you are using does not make assumptions about the distribution of your data, such as k-nearest neighbors and artificial neural networks.

• Standardize, mean of 0 and a std of 1:

  • When the algorithm assumes a gaussian dist, such as linear regression, logistic regression and linear discriminant analysis. LR, LogR, LDA

**Generally, it is a good idea to standardize data that has a Gaussian (bell curve) distribution and normalize otherwise.4. In general terms, we should test 0,1 or -1,1 empirically and possibly match the range to the NN gates/activation function etc.