normalization-and-scaling.md

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.