Monotonic Optimal Binning for Frequency Models
Introduction
To mimic the py_mob package (https://pypi.org/project/py-mob) for binary outcomes, the freq_mob is a collection of python functions that would generate the monotonic binning and perform the variable transformation for frequency outcomes such that the Pearson correlation between the transformed
Should you have any question or suggestion about the freq_mob package, please feel free to drop me a line.
Core Functions
freq_mob
|-- qtl_bin() : An iterative discretization based on quantiles of X.
|-- cnt_bin() : A revised iterative discretization for records with Y > 0.
|-- iso_bin() : A discretization algorthm driven by the isotonic regression between X and Y.
|-- rng_bin() : A revised iterative discretization based on the range of X values.
|-- kmn_bin() : A discretization algorthm based on the kmeans clustering of X.
|-- gbm_bin() : A discretization algorthm based on the gradient boosting machine.
|-- view_bin() : Displays the binning outcome in a tabular form.
|-- cal_newx() : Applies the variable transformation to a numeric vector based on the binning outcome.
`-- mi_score() : Calculates the mutual information score between X and Y.
Authors
WenSui Liu is a seasoned data scientist with 15-year experience in the financial service industry.
Joyce Liu is a college student majoring in Mathematics.