set_catboost_categories: Set categorical feature mappings for CatBoost model

In tidypredict: Run Predictions Inside the Database

Set categorical feature mappings for CatBoost model

Description

CatBoost stores categorical features as hash values internally. This function establishes the mapping between hash values and category names by examining a data frame with the same factor columns used during training.

Usage

set_catboost_categories(parsed_model, model, data)

Arguments

parsed_model	A parsed CatBoost model from parse_model()
model	The original CatBoost model object
data	A data frame containing factor columns matching the categorical features used in the model. The factor levels must match those from training.

Details

This function is only needed when using raw CatBoost models (trained with catboost.train()). When using parsnip/bonsai, categorical features are handled automatically and this function is not required.

Value

The parsed model with category mappings added

Examples

## Not run: 
# For raw CatBoost models with categorical features:
pm <- parse_model(catboost_model)
pm <- set_catboost_categories(pm, catboost_model, training_data)
tidypredict_fit(pm)

# For parsnip/bonsai models, this is not needed:
# tidypredict_fit(parsnip_model_fit)  # works automatically

## End(Not run)

tidypredict documentation built on Aug. 24, 2026, 9:10 a.m.