#### 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 $Log(Y)$ is equal to 1. In case of frequency count models with $Log()$ link function, the transformation is derived as $F(x)_i = Log \frac{\sum_i Y / \sum_i Exposure}{\sum Y / \sum Exposure}$ in the training sample, where $Exposure$ is the number of cases and $i$ refers to the $ith$ bin groupped by $x$ values.
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 value range of X.
|-- 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](mailto:liuwensui@gmail.com) is a seasoned data scientist with 15-year experience in the financial service industry.
[Joyce Liu](mailto:joyce.jl.liu@gmail.com) is a college student majoring in Mathematics with a strong passion for data science.
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