Data Preprocessing for tidylearn — tl_prepare

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)

Value

A list with components:

data: The processed data frame.
original_data: The original unprocessed data frame.
preprocessing_steps: A list of metadata for each preprocessing step applied (imputation values, encoding maps, scaling parameters, etc.).
formula: The formula passed in (or NULL).

Details

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

Examples

# \donttest{
processed <- tl_prepare_data(iris, Species ~ ., scale_method = "standardize")
#> Scaling numeric features using method: standardize
model <- tl_model(processed$data, Species ~ ., method = "tree")
# }