Which binning method allows you to create bins based on a supervising field?

The optimal binning method is designed to create bins based on a supervising field, which means it takes into account the relationship between the independent variable and the dependent variable. This method analyzes the target variable’s distribution across different values of the independent variable and determines the most suitable cuts or boundaries to create bins that maximize the predictive power or minimize the loss in information. As it uses information from the dependent variable, optimal binning effectively categorizes continuous data into discrete intervals, which can improve model performance by accounting for underlying patterns in the data.

In contrast, other methods like fixed-width binning create bins of equal width without considering the target variable. Mean binning creates bins based solely on the average values of the data points. Rank binning organizes data points based on their ranking without regard to the supervising field's distribution. Therefore, optimal binning stands out as the method that directly incorporates the supervising field, making it particularly useful in predictive analytics for tailoring segments effectively.