Interpretable Boosted Linear Models
Density Plot of Beta Corrections for a Variable
Scatter Plot of Beta Corrections for a Variable
Compute Beta Corrections based on SHAP values
Density Plot of Bias Corrections from SHAP values
Check Data Variability for Modeling
Check Object of Class iblm
Plot GLM vs IBLM Predictions with Different Corridors
Create Pre-Configured Beta Corrected Density Plot Function
Create Pre-Configured Beta Corrected Scatter Plot Function
Create Pre-Configured Bias Density Plot Function
Create Pre-Configured Overall Correction Plot Function
Obtain Booster Model Beta Corrections for tabular data
Obtain GLM Beta Coefficients for tabular data
Convert Data Frame to Wide One-Hot Encoded Format
Explain GLM Model Predictions Using SHAP Values
Extract SHAP values from an xgboost Booster model
Calculate Pinball Scores for IBLM and Additional Models
Load French Motor Third-Party Liability Frequency Dataset
Plot Overall Corrections from Booster Component
Predict Method for IBLM
Convert Shap values to Wide One-Hot Encoded Format
Split Dataframe into: 'train', 'validate', 'test'
Custom ggplot2 Theme for IBLM
Train IBLM Model on XGBoost
Train XGBoost Model Using the IBLM Model Parameters
Implements Interpretable Boosted Linear Models (IBLMs). These combine a conventional generalized linear model (GLM) with a machine learning component, such as XGBoost. The package also provides tools within for explaining and analyzing these models. For more details see Gawlowski and Wang (2025) <https://ifoa-adswp.github.io/IBLM/reference/figures/iblm_paper.pdf>.
Useful links