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ml 0.1.2

Fixed

• Per-holdout assess enforcement: ml_assess() now rejects a second call on the same test partition regardless of which model calls it. The provenance registry tracks spent holdouts via content-addressed fingerprinting.
• Fixed ml_prepare() return value extraction (X and norm fields).

New

• ml_cv(), ml_cv_temporal(), ml_cv_group() for cross-validation.
• ml_verify() for post-fit model verification.
• ml_prepare() for explicit preprocessing.
• Content-addressed provenance registry (rlang::hash fingerprinting).
• Cross-verb provenance checks (train/test from same split).

ml 0.1.0

Initial CRAN release.

Core workflow

• Four-verb grammar: ml_split(), ml_fit(), ml_evaluate(), ml_assess(). The evaluate/assess boundary prevents data leakage by separating iterative model selection from final generalization estimates.
• Three-way data splitting (train/valid/test) with automatic stratification for classification targets.
• Mandatory reproducibility: seeds are auto-generated and stored when not provided explicitly.
• Per-fold preprocessing: encoding and scaling fit on training folds only.

Algorithms

• 13 algorithms: logistic regression, linear regression (ridge), decision tree, random forest, extra trees, gradient boosting, histogram gradient boosting, XGBoost, KNN, naive Bayes, SVM, elastic net, AdaBoost.
• Optional backends via Suggests: xgboost, ranger, glmnet, e1071, kknn, naivebayes, rpart. Without these, algorithms fall back to the optional Rust engine or report as unavailable.

Additional verbs

• ml_screen() for rapid algorithm comparison across all available backends.
• ml_tune() for hyperparameter tuning with random search and cross-validation.
• ml_stack() for model ensembling via out-of-fold stacking.
• ml_drift() for data drift detection (KS test).
• ml_shelf() for model staleness monitoring.
• ml_explain() for feature importance (impurity-based and coefficient-based).
• ml_calibrate() for probability calibration (Platt scaling).
• ml_validate() for pass/fail gating against user-defined rules.
• ml_profile() for dataset profiling.
• ml_save() / ml_load() for model persistence in .mlr format.

Rust backend

• Optional compiled Rust engine via extendr bindings. Detected at install time by configure; no Rust required for a fully functional installation.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.