run_rf: Run random forest model with ranger

In ubair: Effects of External Conditions on Air Quality

Run random forest model with ranger

Description

This function trains a random forest model (ranger) on the specified training dataset and makes predictions on the test dataset in a counterfactual scenario. The model uses meteorological variables and temporal features.

Usage

run_rf(train, test, model_params, alpha, calc_shaps)

Arguments

train	Dataframe of train data as returned by the split_data_counterfactual() function.
test	Dataframe of test data as returned by the split_data_counterfactual() function.
model_params	list of hyperparameters to use in ranger call. See ranger:ranger() for options.
alpha	Confidence level of the prediction interval between 0 and 1.
calc_shaps	Boolean value. If TRUE, calculate SHAP values for the method used and format them so they can be visualised with shapviz:sv_importance() and shapviz:sv_dependence(). The SHAP values are generated for a subset (or all, depending on the size of the dataset) of the test data.

Details

Note: Runs the random forest model for individualised use with own data pipeline. Otherwise use run_counterfactual() to call this function.

Value

List with data frame of predictions and model

ubair documentation built on April 12, 2025, 2:12 a.m.