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Auto ML: Automated Machine Learning Workflow

Source: R/workflows.R
tl_auto_ml.Rd

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

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). Controls which models are attempted and whether cross-validation is used for evaluation. The budget is checked between model fits, not during them – once a model starts training it runs to completion because R cannot safely interrupt C-level code (e.g. randomForest, xgboost, e1071).

How the budget shapes the workflow:

  • Under 30s: Only fast models are attempted (tree, logistic/linear). Cross-validation is skipped; models are ranked on training-set metrics only. Expect 2 models in the leaderboard. Use this for quick sanity checks or interactive exploration.

  • 30–120s: All baseline models are attempted including random forest. Cross-validation runs when enough time remains after each model fit; otherwise training metrics are used. Advanced models (SVM, XGBoost / ridge, lasso) are attempted if 40\ remains after baselines. Dimensionality reduction and clustering pipelines run if enabled and 10\

  • 120s+ (recommended): The full pipeline runs – all baselines, advanced models, PCA-augmented variants, and cluster-augmented variants, each with cross-validation. Expect 9–11 models in the leaderboard.

Because individual model fits (especially forest, SVM, XGBoost with CV) can take 5–30s each depending on data size, the actual wall-clock time may modestly exceed the budget by the duration of the last model that was started before the budget expired.

cv_folds

Number of cross-validation folds (default: 5). Reducing this (e.g. to 2 or 3) is an effective way to stay closer to the time budget since CV is typically the most expensive step.

metric

Evaluation metric (default: auto-selected based on task). For classification: "accuracy"; for regression: "rmse".

Value

A list with class "tidylearn_automl" containing:

best_model

The best tidylearn model object

models

Named list of all successfully trained models

leaderboard

Tibble ranking models by the chosen metric, with columns model, score and evaluation. The evaluation column records how each score was obtained – "cv" for cross-validated, "train" for training-set metrics, which are optimistic. Scores of different kinds are not directly comparable; a mixed leaderboard means the budget ran short of cross-validating every model.

task

Detected or specified task type

metric

Metric used for ranking

runtime

Total elapsed time as a difftime object

Examples

# \donttest{
# Quick run with fast models only (< 30s budget skips forest/SVM/XGBoost)
result <- tl_auto_ml(iris, Species ~ .,
  time_budget = 10,
  use_reduction = FALSE,
  use_clustering = FALSE,
  cv_folds = 2)
#> Starting Auto ML with task: classification
#> Time budget: 10 seconds
#> 
#> [1/4] Training baseline models...
#>   Training: baseline_tree
#> 
#> [4/4] Training advanced models...
#> 
#> [*] Creating leaderboard...
#> 
#> Auto ML complete in 0.02 seconds
#> Best model: baseline_tree
result$leaderboard
#> # A tibble: 1 × 3
#>   model         score evaluation
#>   <chr>         <dbl> <chr>     
#> 1 baseline_tree 0.953 cv        
# }