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.

Why Binning?

Though there are some reticence to it [1], the benefits of binning are pretty straight forward:

• It allows missing data and other special calculations (e.g. divided by zero) to be included in the model.
• It controls or mitigates the impact of outliers over the model.
• It solves the issue of having different scales among the characteristics, making the weights of the coefficients in the final model comparable.

Unsupervised Discretization
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

• Objective: Understand the distribution of a variable. 
• Example: The classic histogram, whose bins have equal length that can be calculated using different rules (Sturges, Rice, and others).
• Disadvantage: The number of records in a bin may be too small to allow for a valid calculation, as shown in Table 1.

Table 1. Time on Books and Credit Performance. Bin 6 has no bads, producing indeterminate metrics.

Read full article here. For more about optimum binning, read my new article, here.