tl_prepare_data: Data Preprocessing for tidylearn

In tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem

Data Preprocessing for tidylearn

Description

Unified preprocessing functions that work with both supervised and unsupervised workflows Prepare Data for Machine Learning

Usage

tl_prepare_data(
  data,
  formula = NULL,
  impute_method = "mean",
  scale_method = "standardize",
  encode_categorical = TRUE,
  remove_zero_variance = TRUE,
  remove_correlated = FALSE,
  correlation_cutoff = 0.95
)

Arguments

data	A data frame
formula	Optional formula (for supervised learning)
impute_method	Method for missing value imputation: "mean", "median", "mode", "knn"
scale_method	Scaling method: "standardize", "normalize", "robust", "none"
encode_categorical	Whether to encode categorical variables (default: TRUE)
remove_zero_variance	Remove zero-variance features (default: TRUE)
remove_correlated	Remove highly correlated features (default: FALSE)
correlation_cutoff	Correlation threshold for removal (default: 0.95)

Details

Comprehensive preprocessing pipeline including imputation, scaling, encoding, and feature engineering

Value

A list containing processed data and preprocessing metadata

Examples

processed <- tl_prepare_data(iris, Species ~ ., scale_method = "standardize")
model <- tl_model(processed$data, Species ~ ., method = "logistic")

tidylearn documentation built on Feb. 6, 2026, 5:07 p.m.