Executes the transformation pipeline on input data. Applies each transformation step defined in the model steps file in sequence, modifying the data accordingly.
Arguments
- mod
A model object created by
prepare_model_pipeline.- data
Either a file path (character) to a CSV file containing the input data, or a data frame. The data must contain all columns specified as predictors in the variables file.
Value
The model object with the transformed data added. The transformed
data is accessible via mod$df. This data frame contains:
Original predictor columns from the input data
New columns created by each transformation step (e.g., centered variables, dummy variables, interaction terms, spline terms)
If a logistic-regression step is included, a column named
logistic_N(where N is a positive integer) containing the predicted probabilities
See also
prepare_model_pipeline to prepare the model object
Examples
if (FALSE) { # \dontrun{
# Prepare and run pipeline
mod <- prepare_model_pipeline("path/to/model-export.csv")
mod <- run_model_pipeline(mod, data = "path/to/input-data.csv")
# Access results
head(mod$df)
# Extract predictions from logistic-regression step
predictions <- mod$df[, grep("^logistic_", names(mod$df))]
# Run on data frame
input_df <- read.csv("path/to/data.csv")
mod <- run_model_pipeline(mod, data = input_df)
} # }