tidypredict (version 1.1.0)

parse_model: Converts an R model object into a parsed model

Description

Parses a fitted R model's structure and extracts the components needed to create a dplyr formula for prediction. The parsed model can be serialized (e.g., saved to YAML) and later used to generate predictions without the original model object.

Usage

parse_model(model)

Value

A parsed model object with class parsed_model and a model-specific subclass (e.g., pm_xgb, pm_tree, pm_regression). The object contains:

• $general: List with model metadata including model (model type), type (used for S3 dispatch), version (parsed model format version), and model-specific parameters.

• Model-specific fields containing coefficients, tree structures, etc.

Arguments

model

An R model object.

Parsed model versions

The $general$version field indicates the parsed model format:

• Version 1: Original format. Linear models store coefficients in a data frame. Tree models use flat case_when() expressions where all leaf conditions are at the same level.

• Version 2: Improved coefficient storage for linear models (lm, earth). Tree models still use flat case_when().

• Version 3: Current format. Tree models (rpart, ranger, randomForest, xgboost, lightgbm, catboost, partykit, cubist) use nested case_when() expressions that mirror the tree structure. This produces more efficient SQL and R code because conditions are evaluated hierarchically rather than checking all leaf paths.

When loading a parsed model saved with an older version, tidypredict automatically uses the appropriate formula builder for backwards compatibility.

Model types

Each parsed model has a type that determines the S3 class used for dispatch:

• pm_regression: Linear models (lm, glm, earth, glmnet)

• pm_tree: Single trees and forests (rpart, partykit, ranger, randomForest, cubist)

• pm_xgb: XGBoost gradient boosting models

• pm_lgb: LightGBM gradient boosting models

• pm_catboost: CatBoost gradient boosting models

Examples

library(dplyr)
df <- mutate(mtcars, cyl = paste0("cyl", cyl))
model <- lm(mpg ~ wt + cyl * disp, offset = am, data = df)
parse_model(model)