Data Preprocessing for tidylearn

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). Only its predictors are processed; a column it excludes, such as - id, is returned unchanged.

impute_method

Method for imputing a missing numeric value: "mean", "median" or "mode". A missing categorical value is always filled with the column's most frequent value.

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 record of each step applied (imputation values, encoding maps, scaling parameters, etc.). It is for inspection: no function applies it to new data.

formula

The formula passed in (or NULL).

Details

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

The statistics are learned from, and applied to, the data passed in. Preparing a whole dataset and then splitting it lets the test rows shape the imputation values and scaling their own scores are measured against. To evaluate a model, split first, or use tl_pipeline, which learns its preprocessing inside each resampling fold.

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")
# }