This function generates predictions from an ensemble model consisting of a GLM and an XGBoost model.
Usage
# S3 method for class 'iblm'
predict(object, newdata, trim = NA_real_, type = "response", ...)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.
Value
A numeric vector of ensemble predictions computed as the element-wise product of GLM response probabilities and (optionally trimmed) XGBoost predictions.
Details
The prediction process involves the following steps:
Generate GLM predictions
Generate Booster predictions
If trimming is specified, apply to booster predictions
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
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()
#> num [1:3764] 0.1709 0.3083 0.1078 0.1846 0.0687 ...