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
