Interpretable Boosted Linear Models

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Documentation for package ‘IBLM’ version 1.0.1

• DESCRIPTION file.
• Package NEWS.

Help Pages

beta_corrected_density	Density Plot of Beta Corrections for a Variable
beta_corrected_scatter	Scatter Plot of Beta Corrections for a Variable
beta_corrections_derive	Compute Beta Corrections based on SHAP values
bias_density	Density Plot of Bias Corrections from SHAP values
check_iblm_model	Check Object of Class 'iblm'
correction_corridor	Plot GLM vs IBLM Predictions with Different Corridors
create_beta_corrected_density	Create Pre-Configured Beta Corrected Density Plot Function
create_beta_corrected_scatter	Create Pre-Configured Beta Corrected Scatter Plot Function
create_bias_density	Create Pre-Configured Bias Density Plot Function
create_overall_correction	Create Pre-Configured Overall Correction Plot Function
data_beta_coeff_booster	Obtain Booster Model Beta Corrections for tabular data
data_beta_coeff_glm	Obtain GLM Beta Coefficients for tabular data
data_to_onehot	Convert Data Frame to Wide One-Hot Encoded Format
explain_iblm	Explain GLM Model Predictions Using SHAP Values
extract_booster_shap	Extract SHAP values from an xgboost Booster model
extract_booster_shap.xgb.Booster	Extract SHAP values from an xgboost Booster model
freMTPLmini	French Motor Insurance Claims Dataset
get_pinball_scores	Calculate Pinball Scores for IBLM and Additional Models
load_freMTPL2freq	Load French Motor Third-Party Liability Frequency Dataset
overall_correction	Plot Overall Corrections from Booster Component
predict.iblm	Predict Method for IBLM
shap_to_onehot	Convert Shap values to Wide One-Hot Encoded Format
split_into_train_validate_test	Split Dataframe into: 'train', 'validate', 'test'
theme_iblm	Custom ggplot2 Theme for IBLM
train_iblm_xgb	Train IBLM Model on XGBoost
train_xgb_as_per_iblm	Train XGBoost Model Using the IBLM Model Parameters