Normalization & Scaling
  • 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.
Copy link