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mob (version 0.4.2)

Monotonic Optimal Binning

Description

Generate the monotonic binning and perform the woe (weight of evidence) transformation for the logistic regression used in the consumer credit scorecard development. The woe transformation is a piecewise transformation that is linear to the log odds. For a numeric variable, all of its monotonic functional transformations will converge to the same woe transformation.

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Install

install.packages('mob')

Monthly Downloads

208

Version

0.4.2

License

GPL (>= 2)

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Last Published

July 31st, 2021

Functions in mob (0.4.2)

cal_woe

Perform WoE transformation of a numeric variable
qtl_bin

Monotonic binning by quantile
qcut

Discretizing a numeric vector
pool_bin

Monotonic binning for the pool data
iso_bin

Monotonic binning based on isotonic regression
hmeq

Credit attributes of 5,960 home equity loans
arb_bin

Monotonic binning based on decision tree model
gbm_bin

Monotonic binning based on generalized boosted model
rng_bin

Monotonic binning by quantile based on value range
batch_bin

Apply monotonic binning to all vectors in dataframe
bad_bin

Monotonic binning by quantile with cases Y = 1
batch_woe

Apply WoE transformations to vectors in dataframe
kmn_bin

Monotonic binning based on k-means clustering