What is Binning?
Binning is the term used in scoring modeling for what is also known in Machine Learning as Discretization, the process of transforming a continuous characteristic into a finite number of intervals (the bins), which allows for a better understanding of its distribution and its relationship with a binary variable. The bins generated by the this process will eventually become the attributes of a predictive characteristic, the key component of a Scorecard.
Though there are some reticence to it , the benefits of binning are pretty straight forward:
Unsupervised Discretization divides a continuous feature into groups (bins) without taking into account any other information. It is basically a partiton with two options: equal length intervals and equal frequency intervals.
Equal length intervals