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Random Forest Model Fitting for Transition Models

Source: R/trans_models_rf.R
fit_ranger.Rd

Fits a random forest model using the ranger package for transition modeling. Uses observation-based weighting and stratified downsampling to handle class imbalance.

Usage

fit_ranger(data, num.trees = 100, max.depth = 100, ...)

Arguments

data

A data.table containing the did_transition column and predictor columns (prefixed with "id_pred_")

num.trees

Number of trees to grow in the random forest (default: 100)

max.depth

Maximum depth of each tree (default: 100)

...

Additional arguments passed to ranger::ranger()

Value

A fitted ranger model object, optionally butchered to reduce memory footprint

Details

The function:

  • Uses ranger for efficient random forest implementation

  • Applies observation-based weights (same approach as grrf_filter)

  • Uses stratified downsampling via sample.fraction

  • Returns probability predictions for the positive class

  • Computes variable importance using impurity measure

  • Applies butcher::butcher() if available to reduce model size

Default hyperparameters:

  • num.trees = 500

  • min.node.size = 1