IBLM (version 1.0.2)

predict.iblm: Predict Method for IBLM

Description

This function generates predictions from an ensemble model consisting of a GLM and an XGBoost model.

Usage

# S3 method for iblm
predict(object, newdata, trim = NA_real_, type = "response", ...)

Value

A numeric vector of ensemble predictions computed as the element-wise product of GLM response probabilities and (optionally trimmed) XGBoost predictions.

Arguments

object

An object of class 'iblm'. This should be output by `train_iblm_xgb()`

newdata

A data frame or matrix containing the predictor variables for which predictions are desired. Must have the same structure as the training data used to fit the 'iblm' model.

trim

Numeric value for post-hoc truncating of XGBoost predictions. If NA (default) then no trimming is applied.

type

string, defines the type argument used in GLM/Booster Currently only "response" is supported

...

additional arguments affecting the predictions produced.

Details

The prediction process involves the following steps:

1. Generate GLM predictions

2. Generate Booster predictions

3. If trimming is specified, apply to booster predictions

4. Combine GLM and Booster predictions as per "relationship" described within iblm model object

At this point, only an iblm model with a "booster_model" object of class `xgb.Booster` is supported

See Also

predict.glm, predict.xgb.Booster

Examples

data <- freMTPLmini |> split_into_train_validate_test(seed = 9000)

iblm_model <- train_iblm_xgb(
  data,
  response_var = "ClaimRate",
  family = "poisson"
)

predictions <- predict(iblm_model, data$test)

predictions |> dplyr::glimpse()