tl_auto_ml: High-Level Workflows for Common Machine Learning Patterns

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

High-Level Workflows for Common Machine Learning Patterns

Description

These functions provide end-to-end workflows that showcase tidylearn's ability to seamlessly combine multiple learning paradigms Auto ML: Automated Machine Learning Workflow

Usage

tl_auto_ml(
  data,
  formula,
  task = "auto",
  use_reduction = TRUE,
  use_clustering = TRUE,
  time_budget = 300,
  cv_folds = 5,
  metric = NULL
)

Arguments

data	A data frame
formula	Model formula (for supervised learning)
task	Task type: "classification", "regression", or "auto" (default)
use_reduction	Whether to try dimensionality reduction (default: TRUE)
use_clustering	Whether to add cluster features (default: TRUE)
time_budget	Time budget in seconds (default: 300)
cv_folds	Number of cross-validation folds (default: 5)
metric	Evaluation metric (default: auto-selected based on task)

Details

Automatically explores multiple modeling approaches including dimensionality reduction, clustering, and various supervised methods. Returns the best performing model based on cross-validation.

Value

Best model with performance comparison

Examples

# Automated modeling
result <- tl_auto_ml(iris, Species ~ .)
best_model <- result$best_model
result$leaderboard

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