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fastml version 0.7.8

New features

• Validation Split Resampling: Added resampling_method = "validation_split" to fastml() and train_models(). The holdout proportion is derived from folds as 1 - 1 / folds, with stratification support where applicable.
• Explicit Save Helper: Added exported save_fastml() as the primary helper for persisting fitted fastml objects.

Improvements

• Nested CV Parameter Tracking: Improved nested cross-validation selection so the chosen outer split better follows the final hyperparameter configuration selected from inner results.
• Survival Holdout Plumbing: Holdout evaluation now forwards survival-specific column metadata (start_col, time_col, status_col) through the evaluation path.
• Prediction Model Validation: predict.fastml() now treats native survival and Royston-Parmar model objects as valid prediction targets when flattening and selecting fitted models.
• Safer Task Detection: Survival auto-detection now ignores missing status values when checking for two-level event coding, and numeric auto-detection only upgrades clearly binary numeric outcomes to classification.
• RNG State Restoration: fastml(), train_models(), and bootstrap confidence interval computations now restore the caller’s .Random.seed after execution.
• Documentation Updates: Expanded documentation for folds, flatten_and_rename_models(), and get_best_model_idx() for clearer usage and cleaner package checks.

Bug fixes

• Fixed event_class validation in both fastml() and train_models() so invalid values are rejected consistently.
• Fixed multiclass handling so logistic_reg is converted to multinom_reg before the training loop, avoiding per-iteration mutation and preserving engine parameter transfer.
• Fixed discriminant model specification helpers to use parsnip::discrim_linear() and parsnip::discrim_quad(), resolving dependency warnings caused by referencing unexported discrim objects.
• Fixed default engine resolution by removing duplicate switch entries for survival algorithms such as survreg and royston_parmar.
• Removed package-owned restoration of deleted objects into .GlobalEnv inside sandboxed preprocessing guards, resolving the corresponding R CMD check NOTE about global environment assignments.
• Deprecated save.fastml() in favour of save_fastml() to avoid confusion with a non-generic S3-style naming pattern.
• Removed dead internal statements in model evaluation and selection paths, including unused holdout label handling and stray performance-value expressions.
• Added missing Rd argument documentation for flatten_and_rename_models() and get_best_model_idx(), resolving R CMD check \usage warnings.
• Added regression tests covering event class validation, engine lookup, nested CV parameter selection, multiclass algorithm swapping, and sandbox global-environment protections.

fastml version 0.7.7

New features

• Feature Importance Stability Analysis: Added explain_stability() function to analyze feature importance stability across cross-validation folds. This helps identify features that are consistently important vs. those whose importance varies across different data subsets.
• Store Fold Models: Added store_fold_models parameter to fastml() to optionally store models trained on each CV fold, enabling stability analysis with explain_stability().
• S3 Methods for Stability Objects: Added print.fastml_stability() and plot.fastml_stability() methods for convenient display of stability analysis results.

Improvements

• Unified Explainer Infrastructure: Added fastml_prepare_explainer_inputs() helper function providing consistent data preparation across all explainer methods (explain_dalex(), explain_ale(), plot_ice(), interaction_strength(), surrogate_tree()).
• Positive Class Resolution: Added resolve_positive_class() helper for consistent positive class handling across explainer functions, respecting event_class settings.
• Enhanced explain_dalex(): Major refactoring with robust preprocessing (“baking”) helper that handles three scenarios: no preprocessor, successful baking, and fallback validation for already-processed data.
• Enhanced plot_ice(): Added target_class parameter for classification, improved feature validation with informative error messages, and added warnings for multiclass problems.
• Improved Resampling Metrics Aggregation: Resampling results now properly compute CV statistics (mean and SD across folds) instead of pooled metrics. Fixed grouping attributes that could carry over from fold processing.
• Better Model Validation in Predictions: Added valid_model() helper to properly validate workflow and native survival model types during prediction.

Bug fixes

• Fixed algorithm name matching in predict.fastml() to correctly resolve base algorithm names to their full “algorithm (engine)” format.
• Fixed fold metrics aggregation in guarded resampling to properly ungroup and convert results to plain tibbles.
• Fixed various edge cases in explainer functions when preprocessing pipelines are absent or data is already processed.
• Fixed unit tests across multiple test files for improved reliability and stability.
• Prevented Rplots.pdf files from being created during test execution by adding graphics device suppression to plotting tests.
• Added Rplots.pdf to .gitignore to prevent accidental tracking.

fastml version 0.7.5

Breaking changes

• Removed incomplete or unstable survival backends where correct, leakage-safe behavior could not be guaranteed.

New features

• Full Survival Analysis Support: Added training, resampling, prediction, metric computation, and model summarization for time-to-event outcomes.
• Guarded Survival Resampling: Introduced a workflow enforcing leakage-safe preprocessing, imputation, and model fitting within each resampling split.
• Integrated Brier Score (IBS): Added IBS and expanded survival metric support with flexible time handling and user-configurable summary outputs.
• New Survival Engines: Added support for parametric and semi-parametric models, including Cox, penalized Cox, Royston–Parmar, and flexible parametric survival models.
• Advanced Resampling Strategies: Implemented grouped, blocked, rolling, stratified, and unbiased nested cross-validation.
• Fold-wise Imputation: Added support for advanced imputation during resampling while preventing outcome leakage.
• Engine Parameters: Introduced an engine_params argument to allow passing engine-specific options in a consistent way.
• S3 Methods: Added explicit S3 method annotations for fastml generics.

Improvements

• Multiclass ROC AUC now defaults to macro averaging (tidymodels) and can be configured via multiclass_auc to use macro_weighted class-prevalence weighting.
• Improved robustness of survival predictions, including risk scores, survival probabilities, quantiles, medians, and time estimates.
• Enhanced survival summary outputs with clearer metric alignment and better handling of stratified and time-varying Cox models.
• Improved extraction of predictions and summaries for parametric survival engines.
• Strengthened recipe validation and sandboxing to harden preprocessing isolation and reduce user-induced leakage.
• Improved handling of novel and missing categorical levels during prediction.
• Integrated resampling metadata more tightly into training workflows and summaries.
• Added survival_metric_convention to align survival evaluation defaults with tidymodels conventions when desired.
• Parallel tuning now uses explicit RNG seeding to keep results stable across core counts.

Bug fixes

• Fixed multiple issues in survival label validation, prediction post-processing, and metric computation.
• Corrected survival risk and probability calculations for several engines and model types.
• Fixed log-rank calculation for time-varying Cox models.
• Fixed summary formatting when confidence intervals are unavailable.
• Removed inappropriate confusion matrix warnings for non-classification tasks.
• Fixed edge cases leading to NA survival predictions and early exits during survival time computation.
• Addressed naming collisions and alignment issues in tuning grids and metric selection.

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They may not be fully stable and should be used with caution. We make no claims about them.
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