Monotonic Optimal Binning for Frequency Models
Project description
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 X and Ln(Y) is equal to 1.
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.
Authors
WenSui Liu is a seasoned data scientist with 15-year experience in the financial service industry. Joyce Liu is a college student major in Mathematics.
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