Cladi39 wrote:Thanks for the definition mladen but i need to learn how to do it.
Cladi39
There are following 6 types defined there :
- Standard Score
- Normalizing errors when population parameters are known. Works well for populations that are normally distributed
- Student's t-statistic
- The departure of the estimated value of a parameter from its hypothesized value, normalized by its standard error.
- Studentized residual
- Normalizing residuals when parameters are estimated, particularly across different data points in regression analysis.
- Standardized moment
- Normalizing moments, using the standard deviation {\displaystyle \sigma }\sigma as a measure of scale.
- Coefficient of variation
- Normalizing dispersion, using the mean \mu as a measure of scale, particularly for positive distribution such as the exponential distribution and Poisson distribution.
- Min-max feature scaling
- Feature scaling is used to bring all values into the range [0,1]. This is also called unity-based normalization. This can be generalized to restrict the range of values in the dataset between any arbitrary points {\displaystyle a}a and {\displaystyle b}b, using for example
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For you probably the last is the most interesting : that is much better known as "raw" stochastic (or min/max normalization) than feature scaling. If you apply stochastic to any value, then you are going to get those normalized values