Data Distribution and Feature Scaling Techniques New to AI/ML. My understanding of feature scaling is that its a set of techniques used to counteract the effects of different features having different scales/ranges (which then causes models to incorrectly weight them more/less).
The two most common techniques here that I keep reading about are normalization (adjusting your feature values between 0 and 1) and standardization (adjusting your feature values to have a 0 mean and standard deviation of 1).
From what I can gather, normalization seems to work better for when your data is non-Gaussian/"Bell Curve", whereas standardization is better when it is Gaussian. But nowhere can I find a decent explanation as to why this is the case!
Why does your data distribution affect the efficacy of your feature scaling technique? Why is normalization good for non-Gaussian whereas standardization is? Any edge cases where you'd use standardization on non-Gaussian data? Any other major techniques besides these two?
For instance, I found this excellent paper on characterizing datasets by various distributions. So I'm wondering if there are methods for feature scaling when the data is, say, geometrically distributed, or when its exponentially distributed, etc. And if so, what are they?!
 A: I cannot speak in terms of machine learning, but I can speak in terms of scaling.
From our tag wiki:
tl;dr version first:

normalization refers to scaling all numeric variables in the
  range [0,1], such as using the formula:
  $$x_{new}=\frac{x-x_{min}}{x_{max}-x_{min}}$$
standardization refers to a transform to the data set to have
  zero mean and unit variance, for example using the equation:
  $$x_{new}=\frac{x-\overline{x}}{s}$$

That is, normalization does not rely on the underlying distribution; standardization transforms the data based upon the parameters of a Gaussian distribution.
Fuller explanations:

"Normalization" refers to several related processes:
  
  
*
  
*("Feature scaling") A set of numbers whose maximum is $M$ and minimum is $m$ can be converted to the range from $0$ to $1$ by means
  of an affine transformation (which amounts to changing their units of
  measurement) $x \to (x-m)/(M-m)$.
  
*A set of positive numbers $\{p_i\}$ representing probabilities or weights can be uniformly rescaled to sum to unity: divide each $p_i$
  by the sum of all the $p_i$.
  
*Analogously, a distribution (or indeed any non-negative function with a finite nonzero integral) can be normalized to have a unit
  integral by dividing its values by the integral.
  
*A vector in a normed linear space is normalized (to unit length) by dividing it by its norm.  This is a general procedure encompassing the
  two preceding operations as special examples.
The range from $0$ to $1$ can be made from  $0$ to any desired limit
  $\alpha$ by multiplying a previously unit-normalized value by
  $\alpha$.
Other kinds of operations exist having a similar intent of
  re-expressing values in a predetermined range.  Many of these are
  nonlinear and tend to be used in specialized settings.

Standardization:

Shifting and rescaling data to assure zero mean and unit variance.
Specifically, when $(x_i), i=1, \ldots, n$ is a batch of data, its
  mean is $m=(\sum_i x_i)/n$ and its variance is $s^2 =
> v=(\sum_i(x_i-m)^2)/\nu$ where $\nu$ is either $n$ or $n-1$ (choices
  vary with application).  Standardization replaces each $x_i$ with $z_i
> = (x_i-m)/s$.

A: It's not depend on Gaussian distribution,its depend on the MODEL that used this features.
The result of standardization (or Z-score normalization) is that the features will be rescaled so that they’ll have the properties of a standard normal distribution with μ=0 and σ=1 that help in different cases such as when you want to compute measure the distance between to variable with different units,or more important one,when your algorithm use need this,for example when it use gradient descent,if the features not on same scale,some of features may update faster then the others.
these algorithms like:


*

*k-nearest neighbors with an Euclidean distance measure 

*k-means (see k-nearest neighbors)

*logistic regression, SVMs, perceptrons, neural networks etc
in the other hand we have Z-score normalization (or standardization) is the so-called Min-Max scaling.in this approach, the data is scaled to a fixed range - usually 0 to 1(not always).
now the question :
Z-score standardization or Min-Max scaling?
There is no obvious answer to this question: 



it really depends on the application.

i have some example for you : 
in clustering analyses, standardization may be especially crucial in order to compare similarities between features based on certain distance measures. Another prominent example is the Principal Component Analysis, where we usually prefer standardization over Min-Max scaling, since we are interested in the components that maximize the variance.
However, this doesn’t mean that Min-Max scaling is not useful at all! A popular application is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.
