# Normalization & Scaling

1. [**A comparison of normalization / scaling techniques in sklearn**](http://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#sphx-glr-auto-examples-preprocessing-plot-all-scaling-py)
2. [**Another great explanation on sklearn and (general) scaling**](http://benalexkeen.com/feature-scaling-with-scikit-learn/) **- normal, min max, etc..**
3. [**Normalization\standardize features** ](http://machinelearningmastery.com/normalize-standardize-machine-learning-data-weka/)

* **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.**