IBLM

Theory

The overall process for fitting and interpreting an IBLM is as follows:

Step 1: Fit a GLM

\[ g(\mu) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \cdots + \beta_n x_n \]

Where g(·) is the link function, μ is the expected value of the response, and β values are your standard regression coefficients.

\[ \text{GLM prediction} = g^{-1} (\beta_0 + \beta_1 x_1 + \beta_2 x_2 + \cdots + \beta_n x_n) \]

Where β values are your standard regression coefficients.

Step 2: Train a Booster on GLM’s Errors

Train a booster model (i.e. XGBoost) on the residuals of the actual response values against the GLM predictions.

Step 3: Use SHAP to Break Down the Booster

SHAP decomposition allows the booster’s prediction to be apportioned into contributions from each feature:

\[ \text{Booster prediction} = g^{-1} (\varphi_0 + \varphi_1(x) + \varphi_2(x) + \cdots + \varphi_n(x)) \]

Where $\varphi_j(x)$ is how much feature j contributed to a specific prediction.

Step 4: Convert SHAP Values to Beta Coefficient Corrections

Transform each SHAP contribution into beta corrections:

\[ \alpha_j(x) \approx \frac{\varphi_j(x)}{x_j} \]

There are two situations where the value of $\alpha_j(x)$ is set to zero and the value for $\varphi_j(x)$ is added to the intercept instead. This is when $x$ is a numerical variable, and the value is zero. Or, when $x$ is a categorical variable, and the value is that of the reference level².

\[ \begin{aligned} \alpha_j(x) &= 0 \text{ when } \begin{cases} x_j = 0 & \text{(numerical)} \\ x_j = \text{ref} & \text{(categorical)} \end{cases} \\[1em] \alpha_0(x) &= \sum_{\substack{j: x_j = 0 \\ \text{or } x_j = \text{ref}}} \varphi_j(x) \end{aligned} \]

Step 5: Combine

The final IBLM can be interpreted as a collection of adjusted GLMs. Each row of the data will essentially have its own GLM coefficients. These are the addition of:

The GLM coefficients derived in step 1, which are the same for all rows
The beta corrections derived in step 4, which are unique to that predictor variable combination.

\[ g(\mu) = (\beta_0 + \varphi_0 + \alpha_0) + (\beta_1 + \alpha_1(x))x_1 + (\beta_2 + \alpha_2(x))x_2 + \cdots + (\beta_n + \alpha_n(x))x_n \]

\[ \begin{aligned} g(\mu) &= \beta'_0 + \beta'_1 x_1 + \beta'_2 x_2 + \cdots + \beta'_n x_n \\ \text{where } \beta'_j &= \beta_j + \alpha_j(x) \\ \text{and } \beta'_0 &= \beta_0 + \varphi_0 + \alpha_0 \end{aligned} \]

Where g(·) is the link function, μ is the expected value of the response, and β values are your standard regression coefficients.

The IBLM prediction is a combination of components.

\[ \begin{aligned} \text{IBLM prediction} &= g^{-1} (\beta'_0 + \beta'_1 x_1 + \beta'_2 x_2 + \cdots + \beta'_n x_n) \\[0.5em] &= \begin{cases} \text{GLM prediction} \times \text{Booster prediction} & \text{when } g = \log \\ \text{GLM prediction} + \text{Booster prediction} & \text{when } g = \text{identity} \end{cases} \end{aligned} \]

This preserves the familiar GLM form while incorporating the booster’s superior predictive power.

Train

To train an IBLM we must get our data into an appropriate format. In this document our demonstrations will be completed using French motor claims dataset freMTPL2freq.

Data for training an IBLM model must be in the form of a list of 3 dataframes. These will be named “train”, “validate” and “test”. The function split_into_train_validate_test() can conveniently split a single dataframe into such a structure.

Code

df <- load_freMTPL2freq()

df <- df |> mutate(ClaimNb = round(ClaimNb)) 

df_list <- df |> split_into_train_validate_test(seed = 1)

There is currently only one function available to train an IBLM. This is train_iblm_xgb() which will use xgboost as the booster model. This performs steps 1 and 2 of the Theory.

The output is of class “iblm”. Objects of this class contain two component models “glm_model” and “booster_model”. There are also other items containing information (see Value section of train_iblm_xgb() for more information)

Code

iblm_model <- train_iblm_xgb(
  df_list,
  response_var = "ClaimNb",
  family = "poisson",
  params = list(seed = 1)
)

class(iblm_model)
#> [1] "iblm"

For convenience, it is also possible to train an XGBoost model using the same settings that were used for you model of class “iblm”. This can be useful when directly comparing (for example see Pinball Score)

Code

xgb_model <- train_xgb_as_per_iblm(iblm_model)

is_identical_config <- purrr::map2_lgl(
  xgboost::xgb.config(xgb_model) |> unlist(),
  xgboost::xgb.config(iblm_model$booster_model) |> unlist(),
  identical
) 

# the config is mostly identical. In our example the differences are:
is_identical_config[!is_identical_config] |> names()
#> [1] "learner.gradient_booster.gbtree_model_param.num_trees"
#> [2] "learner.learner_model_param.base_score"

Explain

Code to explain and interpret an object of class “iblm” are conveniently wrapped in the explain_iblm() function. This performs steps 3, 4 and 5 of the Theory. The values derived are all based on the data fed in, which would usually be the test portion of your dataset.

The following line is all that is required:

Code

ex <- explain_iblm(iblm_model, df_list$test)

The output object is a list containing the following items:

beta_corrected_scatter Function to create scatter plots showing SHAP corrections vs variable values (see beta_corrected_scatter())
beta_corrected_density Function to create density plots of SHAP corrections for variables (see beta_corrected_density())
bias_density Function to create density plots of SHAP corrections migrated to bias (see bias_density())
overall_correction Function to show global correction distributions (see overall_correction())
shap Dataframe showing raw SHAP values of data records. These are the $\varphi_n(x)$ values described in step 3 of Theory.
beta_corrections Dataframe showing beta corrections (in wide/one-hot format) of data records. These are the $\alpha_0(x)$ and $\alpha_j(x)$ values described in step 4 of Theory.
data_beta_coeff Dataframe showing corrected beta coefficients of data records. These are the $\beta'_0$ and $\beta'_j$ values described in step 5 of Theory. For categorical variables, the value corresponds to the relevant coefficient for that datapoint.

Many of the items output are functions. The functions can then be called to observe the components of the “iblm” object in different ways.

Beta Corrected Density

The corrected beta coefficients (β’ⱼ) can be observed in a density plot. This plotting function allows the user to see corrected beta coefficient values for a particular variable.

The density plot also shows the standard error for the original beta coefficient, fitted in the GLM model component. This can be useful to understanding the significance of the range in corrected beta coefficients.

Numerical Variables

For numerical variables, calling the function creates a single plot. Below we show examples with different numerical variables from our freMTPL2freq dataset.

VehPower

Code

ex$beta_corrected_density(varname = "VehPower")

VehAge

Code

ex$beta_corrected_density(varname = "VehAge")

DrivAge

Code

ex$beta_corrected_density(varname = "DrivAge")

BonusMalus

Code

ex$beta_corrected_density(varname = "BonusMalus")

Categorical Variables

For categorical variables, calling the function creates a list of ggplot objects. A separate plot is produced for each level of that categorical variable. Note this does not include the reference level.

In the example below “VehBrand” has 11 levels and produces 10 plots. Level “B1” was a reference level and does not have a beta coefficient within the GLM component. Instead, for “B1”, the SHAP values are migrated to the bias (see Bias Corrected Density). Also in the example, we have wrapped our list using patchwork to create a single graphic for simplicity.

Code

VehBrand <- ex$beta_corrected_density(varname = "VehBrand", type = "hist")

VehBrand |> patchwork::wrap_plots(ncol = 2)

Beta Corrected Scatter

The corrected beta coefficients (β’ⱼ) can be observed in a scatter plot (or boxplot). This plotting function allows the user to see corrected beta coefficient values for a particular variable and the relationship to that particlar variable value.

Numerical Variables

When varname is set to a numerical variable, a scatter plot is produced. The example below shows the relationship of corrected beta coefficient values against driver age. Note that the original beta coefficient fitted to the GLM component is shown by a dashed black line (and stated near the top of the plot in orange). We can see that corrected beta coefficients can be materially different. There is also a smoothed trend line fitted that shows the overall pattern of correction.

Note the color argument can also be set to try and observe interactions with a second variable.

Code

ex$beta_corrected_scatter(varname = "DrivAge", color = "VehPower")
#> `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

Categorical Variables

When varname is set to a categorical variable, a boxplot is produced. This shows the distribution for each individual level within that categorical variable.

Note the color argument can also be set to try and observe interactions with a second variable.

Code

ex$beta_corrected_scatter(varname = "VehBrand")

Bias Corrected Density

As described in step 4 of Theory, some shap values cannot be translated to beta corrections. These values are instead migrated to the bias. There are two situations where this can occur:

Numerical variable where the value was zero for that data point.
Categorical variable where the value was the reference level for that data point.

In the bias_correction_var plot we visualise all the individual times this occured in our test dataset. For categorical variables Area, Region, VehBrand and VehGas the total counts in the repsective facets of the histogram will equal the total occurences of the reference level for that variable. For numerical variable VehAge the total count in that facet will equal the total occurences of zero for VehAge in the dataset. Other numerical variables are dropped from the plot as they had no instances of zero value (for example DrivAge)

The bias_correction_total plot is the cumulative sum of these components across each data point. Note any data point with no bias migration (i.e. conditions 1 and 2 do not occur) will have the standard bias adjustment and is not counted in the plot. The standard bias value, along with standard error around this, can be observed as the straight blue line and dashed orange lines either side.

The bias values can be observed with bias_density().

Variable

Code

bias_corrections <- ex$bias_density()
bias_corrections$bias_correction_var

Total

Code

bias_corrections <- ex$bias_density()
bias_corrections$bias_correction_total

Overall Correction

The distribution of overall corrections across your test data can be observed. If the link function is log then the correction is a multiplier (as is the case below). If the link function is identity, then the overall correction is an addition. By default the x scale is transformed by the link function, but this can be switched off.

Link Transformed

Code

ex$overall_correction()

Not transformed

Code

ex$overall_correction(transform_x_scale_by_link = FALSE)

Predict

The package contains a predict method for the class “iblm”. This employs the predict() method of both the “glm_model” and “booster_model” items within the “iblm” class object.

Code

predictions <- predict(iblm_model, df_list$test)

The predict method contains an option to trim. This truncates the values of the booster model predictions to prevent extreme deviation from the foundation GLM. This is explored further in Correction Corridor.

Because IBLM is essentially a collection of GLMs, and the corrected beta coefficients are derived by the explain_iblm() function, it is also possible to predict through linear calculation.

Code

coeff_multiplier <- 
  df_list$test |>
  select(-all_of("ClaimNb")) |>
  mutate(
    across(
      all_of(iblm_model$predictor_vars$categorical),
      ~1
      )
    ) |>
  mutate(bias = 1, .before = 1)

predictions_alt <- 
  (ex$data_beta_coeff * coeff_multiplier) |>
  rowSums() |> 
  exp() |>
  unname()

# difference in predictions very small between two alternative methods
range(predictions_alt / predictions - 1)
#> [1] -9.531663e-07  9.788184e-07

Pinball Score

The pinball score is a way to measure the performance of the models. It shows the increase (or decrease when negative) of predictability compared to using the training data mean instead.

In the example below, we see the iblm has increased score compared to the glm. We have also added the xgb_model (derived in section Train) as a further comparison.

Code

get_pinball_scores(
  data = df_list$test, 
  iblm_model = iblm_model,
  additional_models = list(xgb = xgb_model)
  ) |> 
  gt() |> 
  fmt_percent("pinball_score")

model	poisson_deviance	pinball_score
homog	1.407148	0.00%
glm	1.348394	4.18%
iblm	1.234015	12.30%
xgb	1.227481	12.77%

Correction Corridor

As described in Predict it is possible to trim the contribution from the booster component of an iblm object when predicting. This prevents estimates deviating too far from the GLM foundational model.

The correction corridor function applies predict.iblm() with different settings of trim, and plots the results. It is also possible to set the color to a variable to observe any patterns. Note that when the trim value is zero, the IBLM predictions are the same as the GLM predictions.

Code

correction_corridor(
  iblm_model, 
  df_list$test,
  trim_vals = c(0.5, 0),
  sample_perc = 0.1,
  color = "DrivAge")

As of v.1.0.0 the IBLM package can only fit a booster model of type XGBoost, however we are looking to add other options in the future.↩︎
The reference level of a categorical variable is the value that is the value for which there is no coefficient in the GLM. The reference levels of an “iblm” class object can be found in .$cat_levels$reference↩︎

IBLM

Karol Gawlowski and Paul Beard

2025-12-16

Installation

Introduction

Theory

Train

Explain

Beta Corrected Density

Numerical Variables

VehPower

VehAge

DrivAge

BonusMalus

Categorical Variables

Beta Corrected Scatter

Numerical Variables

Categorical Variables

Bias Corrected Density

Variable

Total

Overall Correction

Link Transformed

Not transformed

Predict

Pinball Score

Correction Corridor