Contents
What are binned variables?
Definition. A Binned Variable (also Grouped Variable) in the context of Quantitative Risk Management is any variable that is generated via the discretization of Numerical Variable into a defined set of bins (intervals).
Why do we categorize continuous variables?
By categorizing the variable into ranges, part of the non-monotonicity can be taken into account in the regression. Hence, categorization of continuous variables can be useful to model non-linear effects into linear models.
How do you divide a continuous variable into a category?
A Median Split is one method for turning a continuous variable into a categorical one. Essentially, the idea is to find the median of the continuous variable. Any value below the median is put it the category “Low” and every value above it is labeled “High.”
What are the problems with binning continuous variables?
See Frank Harrell’s page here for a long list of problems with binning continuous variables. If you use a few bins you throw away a lot of information in the predictors; if you use many you tend to fit wiggles in what should be a smooth, if not linear, relationship, & use up a lot of degrees of freedom.
What is the benefit of breaking up a continuous predictor?
If you use a few bins you throw away a lot of information in the predictors; if you use many you tend to fit wiggles in what should be a smooth, if not linear, relationship, & use up a lot of degrees of freedom. Generally better to use polynomials ( x + x 2 + …) or splines (piecewise polynomials that join smoothly) for the predictors.
Which is better dichotomization or a continuous predictor?
Sometimes a distribution naturally lends itself to a set of classes, in which case dichotomization will actually give you a higher degree of accuracy than a continuous function. Also, as previously mentioned, depending on the audience, the ease of presentation can outweigh the losses to accuracy.
What’s the benefit of crude Binning in modelling?
Crude binning makes your model only approximately generalizable. In general, if you have questions about the behaviour of the response between cut-offs, fit the best model you can first, & then use it to answer them.] [With regard to presentation; I think this is a red herring: (1) Ease of presentation doesn’t justify bad modelling decisions.