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scorecardModelUtils (version 0.0.0.9)

Credit Scorecard Modelling Utils

Description

Provides infrastructure functionalities such as missing value treatment, information value calculation, GINI calculation etc. which are used for developing a traditional credit scorecard as well as a machine learning based model. The functionalities defined are standard steps for any credit underwriting scorecard development, extensively used in financial domain.

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Version

Install

install.packages('scorecardModelUtils')

Monthly Downloads

194

Version

0.0.0.9

License

GPL-2 | GPL-3

Maintainer

Arya Poddar

Last Published

April 27th, 2018

Functions in scorecardModelUtils (0.0.0.9)

Clubbing of classes of categorical variable with low population percentage into one class

gradient_boosting_parameters

Hyperparameter optimisation or parameter tuning for Gradient Boosting Regression Modelling by grid search

Scoring a dataset with class based on a scalling logic to arrive at final score

Variable reduction based on Information Value filter

Converting coefficients of logistic regression into scores for scorecard building

WOE and IV table for list of numerical and categorical variables

Random sampling of data into train and test

random_forest_parameters

Hyperparameter optimisation or parameter tuning for Random Forest by grid search

Missing value imputation

Cramer's V value between two categorical variables

Clubbing class of a categorical variable with low population percentage with another class of similar event rate

dtree_split_val

Getting the split value for terminal nodes from decision tree

Recursive Decision Tree partitioning with monotonic event rate along with IV table for individual numerical variable

Clubbing class of categorical variables with low population percentage with another class of similar event rate

Creates random index for k-fold cross validation

IV table for individual categorical variable

Creates confusion matrix and its related measures

Variable reduction based on Cramer's V filter

Pairwise Cramer's V among a list of categorical variables

Computes error measures between observed and predicted values

Calculating mode value of a vector

Redefines target value

Binning numerical variables based on cuts from IV table

support_vector_parameters

Hyperparameter optimisation or parameter tuning for Suppert Vector Machine by grid search

Univariate analysis of variables

Removing multicollinearity from a model using vif test

Performance measure table with Gini coefficient, KS-statistics and Gini lift curve