Why do we reduce the number of histogram bins from 10 to 2 in predictive models?

Reducing the number of histogram bins from 10 to 2 in predictive models is often done to simplify the classification of customer behavior into broader categories. By grouping data into fewer bins, it becomes easier to understand general trends within the data and to make actionable decisions based on these trends.

When the bins are reduced to just two, the model can effectively classify customers into two distinct groups, such as "Loyal" and "Churn." This simplification allows for a more focused approach to decision-making and customer targeting, facilitating the creation of tailored strategies that can address the specific needs of each customer group.

In a practical application, using just two bins helps organizations streamline their processes and make clear, actionable decisions based on the identified characteristics of each group, which inhibits complexity and aids in implementation. This can be especially useful in marketing strategies where clear distinctions are necessary for targeting and messaging.