Arbitrary Binning

In arbitrary binning, the bins limits are defined arbitrarily based on the domain knowledge or specific requirements of the problem. Unlike equal-width or equal-frequency binning, the bin boundary values are not determined by the data itself but rather by the data analyst or the problem domain.

Arbitrary binning is useful when the data has a specific meaning or context that cannot be captured by other binning methods. For example, on an e-commerce website, we might group customers into different categories based on their shopping habits or purchase history. This would involve creating custom bins that are relevant to the problem rather than relying on statistical criteria to determine the bin boundaries.

There is no specific mathematical formula for arbitrary binning, as the bin boundaries are determined by the specific requirements of the problem or domain knowledge. In practice, arbitrary binning involves selecting bin boundaries based on specific criteria, such as ranges of values, meaningful categories, or business rules.

To give an example, suppose we are analyzing the income levels of people in a specific region. We might define arbitrary bins based on the following income ranges: “low income” for incomes less than $30,000, “middle income” for incomes between $30,000 and $70,000, and “high income” for incomes above $70,000. In this case, the bins are determined by the specific problem domain, and the bin boundaries are not determined by any statistical criteria.

Advantages and Disadvantages

Let’s discuss the advantages and limitations of binning arbitrarily:

Advantages

Arbitrary binning provides flexibility in defining the bin boundaries based on the specific requirements of the problem, which can be more meaningful and relevant than automatically determined bins.

Disadvantages

The bin boundaries are determined manually, which can introduce bias into the analysis.