R: Credit Scorecard Modelling Utils

# Credit Scorecard Modelling Utils

## Documentation for package ‘scorecardModelUtils’ version 0.0.1.0

- DESCRIPTION file

## Help Pages

categorical_iv | IV table for individual categorical variable
cat_new_class | Clubbing class of categorical variables with low population percentage with another class of similar event rate
club_cat_class | Clubbing class of a categorical variable with low population percentage with another class of similar event rate
cv_filter | Variable reduction based on Cramer's V filter
cv_table | Pairwise Cramer's V among a list of categorical variables
cv_test | Cramer's V value between two categorical variables
dtree_split_val | Getting the split value for terminal nodes from decision tree
dtree_trend_iv | Recursive Decision Tree partitioning with monotonic event rate along with IV table for individual numerical variable
fn_conf_mat | Creates confusion matrix and its related measures
fn_cross_index | Creates random index for k-fold cross validation
fn_error | Computes error measures between observed and predicted values
fn_mode | Calculating mode value of a vector
fn_target | Redefines target value
gini_table | Performance measure table with Gini coefficient, KS-statistics and Gini lift curve
gradient_boosting_parameters | Hyperparameter optimisation or parameter tuning for Gradient Boosting Regression Modelling by grid search
iv_filter | Variable reduction based on Information Value filter
iv_table | WOE and IV table for list of numerical and categorical variables
missing_val | Missing value imputation
num_to_cat | Binning numerical variables based on cuts from IV table
others_class | Clubbing of classes of categorical variable with low population percentage into one class
random_forest_parameters | Hyperparameter optimisation or parameter tuning for Random Forest by grid search
sampling | Random sampling of data into train and test
scalling | Converting coefficients of logistic regression into scores for scorecard building
scoring | Scoring a dataset with class based on a scalling logic to arrive at final score
support_vector_parameters | Hyperparameter optimisation or parameter tuning for Suppert Vector Machine by grid search
univariate | Univariate analysis of variables
vif_filter | Removing multicollinearity from a model using vif test