When including predictor variables in logistic regression, they may need to be a

Question

When including predictor variables in logistic regression, they may need to be altered or transformed prior to inclusion in a model. One type of transformation might be to normalize predictors so that each variable is on the same scale. This ensures that no predictor variable is overweighted in analysis simply because its natural range has larger numbers. When discussing logistic regression, we also addressed two other types of transformations to predictor variables: (1) binning and (2) x^2 transformations. For both binning and x^2 transformations describe one specific instance or one reason why each transformation may be necessary.

Explanation / Answer

Binning or discretization is the process of transforming numerical variables into categorical counterparts. An example is to bin values for Age into categories such as 20-39, 40-59, and 60-79. Numerical variables are usually discretized in the modeling methods based on frequency tables (e.g., decision trees). Moreover, binning may improve accuracy of the predictive models by reducing the noise or non-linearity.

x^2 transformation:

The square, x to x2x2, has a moderate effect on distribution shape and it could be used to reduce left skewness. In
practice, the main reason for using it is to fit a response by a
quadratic function y=a+bx+cx2y=a+bx+cx2. Quadratics have a turning
point, either a maximum or a minimum, although the turning point in a function fitted to data might be far beyond the limits of the
observations. The distance of a body from an origin is a quadratic if that body is moving under constant acceleration, which gives a very
clear physical justification for using a quadratic.

Overall we can say that to study the scatter in a more decentralized or distributed way we can use the x^2 transformations for a better view or understanding. But while dealing with large values binning is a better option as the graphs/distributions are dramatically separated in x^2 transformation making it hard to analyze or predict.

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