Data binning is a data pre-processing technique used to reduce the effects of minor observation errors. The original data values which fall in a given small interval, a bin, are replaced by a value representative of that interval, often the central value. It is a form of quantization.
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.
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