preprocess_data: Preprocess data before model fitting

In classbound: Visualization for Classification Decision Boundaries

Preprocess data before model fitting

Description

Applies standard preprocessing steps to a training dataset: validates structure, coerces character columns to factors, drops unused factor levels, rejects missing and infinite values, and converts response labels to a factor.

Usage

preprocess_data(data, labels = NULL, ...)

Arguments

data	A data frame of raw training data.
labels	Optional vector of target labels. Converted to a factor with unused levels dropped.
...	Additional arguments (currently unused).

Details

This function is called automatically by fit_model(). Do not call it manually before fit_model(), because doing so will result in feature metadata being extracted from the pre-processed data rather than the original data, which corrupts the imputation values used by boundary_compute() for 2D slicing.

It is exported for use in custom workflows and testing, but it is not typically needed in the standard fit_model() → boundary_compute() pipeline.

Value

A list with two elements:

• ⁠$data⁠: the preprocessed data frame

• ⁠$labels⁠: the processed factor labels (or NULL if not supplied)

classbound documentation built on Sept. 30, 2026, 5:13 p.m.