Binning-Discretization--Quantile-Binning--KMeans-Binning

credits: bit.ly/38qpuDB

binning:Discretization is the process of transforming continuous variables into discrete variables by creating a set of contiguous intervals that span the range of the variable's values .discretization is also called binningwe use binning to

i) To handle outliers ii)To improve the value spread

Quantile-Binning: Quantile binning aims to assign the same number of observations to each bin, if the number of observations is evenly divisible by the number of bins. As a result, each bin should have the same number of observations, provided that there are no tied values at the boundaries of the bins.

KMeans-Binning: Ckmeans allows to cluster numeric data (on one dimension) into groups with the least within-group sum-of-squared-deviations. We'll use simple-statistics' very fast implementation of this algorithm in Plot.binX's transform: # of thresholds.