# Normalization & Scaling

- 2.

**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.**

Last modified 1yr ago