Introduction to Binning
The Binning stage performs what is known as unsupervised binning, which divides a continuous variable into groups (bins) without taking into account objective information. The data captured includes ranges, quantities, and percentage of values within each range.
- It allows records with missing data 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.
In Spectrum™ Technology Platform unsupervised binning, you can use equal-width bins, where the data is divided into bins of equal size, or equal-frequency bins, where the data is divided into groups containing approximately the same number of records. In the Binning stage, equal-width bins are referred to as Equal Range bins and equal-frequency bins are referred to as Equal Population bins.
You can view a list of binnings and delete binnings using command line instructions. See "Machine Learning Module" in the Administration Utility section of the Administration Guide.