Binning Variables to Use in Logistic Regression
Checking the performance of the bins created on test data
Add binned variables to data
Force a numerical variable to follow a monotonically decreasing trend
Force a numerical variable to follow a monotonically increasing trend
Bins variables to be used in logistic regression
Simulated default data of 100 customers
Split a variable based on specified cuts
Combine NA bins
Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.