I have Data Frame where are continuous values Features present. I want to bin these Features in category. I am using
KBinsDescretizer for this. To find the optimal number of bins i used
Kmeans "Elbow-Method" and feed the output in
But, is it the right method to find the perfect number of bins? I looked in internet and came across "Freedman-Diaconis" method and also some others like "Sturges's". But, these are used to find the optimal number of bins in histogramm.
What is the right way here? My Parameters in
KBinsDescritizer are :
(n_bins=(func_kmeans_elbow_method) , encode='oridnal', strategy='kmeans') # is it a good choice here to use 'kmeans' or 'quantile'