Credit Scorecard Modelling Utils
Clubbing class of categorical variables with low population percentage...
IV table for individual categorical variable
Clubbing class of a categorical variable with low population percentag...
Variable reduction based on Cramer's V filter
Pairwise Cramer's V among a list of categorical variables
Cramer's V value between two categorical variables
Getting the split value for terminal nodes from decision tree
Recursive Decision Tree partitioning with monotonic event rate along w...
Creates confusion matrix and its related measures
Creates random index for k-fold cross validation
Computes error measures between observed and predicted values
Calculating mode value of a vector
Redefines target value
Performance measure table with Gini coefficient, KS-statistics and Gin...
Hyperparameter optimisation or parameter tuning for Gradient Boosting ...
Variable reduction based on Information Value filter
WOE and IV table for list of numerical and categorical variables
Missing value imputation
Binning numerical variables based on cuts from IV table
Clubbing of classes of categorical variable with low population percen...
Hyperparameter optimisation or parameter tuning for Random Forest by g...
Random sampling of data into train and test
Converting coefficients of logistic regression into scores for scoreca...
Scoring a dataset with class based on a scalling logic to arrive at fi...
Hyperparameter optimisation or parameter tuning for Suppert Vector Mac...
Univariate analysis of variables
Removing multicollinearity from a model using vif test
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.