tidylearn (version 0.1.0)

tl_auto_ml: 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
)

Value

Best model with performance comparison

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.

Examples

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