Supervised Learning with Mandatory Splits and Seeds

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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