R: Supervised Learning with Mandatory Splits and Seeds

# Supervised Learning with Mandatory Splits and Seeds

## Documentation for package ‘ml’ version 0.1.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

ml | The ml module — all verbs accessed via ml$verb()
ml_algorithms | List available ML algorithms
ml_assess | Assess model on held-out test data (do once)
ml_best | Get the best model from a leaderboard
ml_calibrate | Calibrate predicted probabilities
ml_check | Verify bitwise reproducibility for a given dataset
ml_check_data | Pre-flight data quality checks
ml_compare | Compare pre-fitted models on the same data
ml_config | Configure ml package settings
ml_cv | Create k-fold cross-validation from a split
ml_cv_group | Create group-aware cross-validation from a split
ml_cv_temporal | Create temporal cross-validation from a split
ml_dataset | Load a built-in dataset
ml_drift | Detect data drift between reference and new data
ml_embed | Embed texts into numeric features
ml_enough | Learning curve analysis - do you need more data?
ml_evaluate | Evaluate model on validation data (iterate freely)
ml_explain | Explain model via feature importance
ml_fit | Fit a machine learning model
ml_leak | Detect potential data leakage
ml_load | Load a model from disk
ml_optimize | Optimize decision threshold for binary classification
ml_plot | Visual diagnostics for a fitted model
ml_predict | Predict from a fitted model (ml_predict style)
ml_predict_proba | Predict class probabilities
ml_prepare | Prepare data for ML: encode, impute, and scale
ml_profile | Profile data before modeling
ml_quick | One-call workflow: split + screen + fit + evaluate
ml_report | Generate an HTML training report
ml_save | Save a model to disk
ml_screen | Screen all algorithms on your data
ml_shelf | Check if a model is past its shelf life
ml_split | Split data into train/valid/test partitions or cross-validation folds
ml_split_group | Split data with group non-overlap — no group leaks across partitions
ml_split_temporal | Split data chronologically — no future leakage
ml_stack | Ensemble stacking
ml_tune | Tune hyperparameters via random or grid search
ml_validate | Validate model against rules and/or baseline
ml_verify | Verify provenance integrity of a model
predict.ml_model | Predict from a fitted model
predict.ml_tuning_result | Predict from best model in a tuning result
print.ml_cv_result | Print ml_cv_result
print.ml_drift_result | Print ml_drift_result
print.ml_embedder | Print ml_embedder
print.ml_evidence | Print ml_evidence
print.ml_explanation | Print ml_explanation
print.ml_leaderboard | Print ml_leaderboard
print.ml_metrics | Print ml_metrics
print.ml_model | Print an ml_model
print.ml_profile_result | Print ml_profile_result
print.ml_shelf_result | Print ml_shelf_result
print.ml_split_result | Print an ml_split_result
print.ml_tuning_result | Print an ml_tuning_result
print.ml_validate_result | Print ml_validate_result