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.save_fastml() as the primary helper for persisting fitted
fastml objects.start_col,
time_col, status_col) through the evaluation
path.predict.fastml() now treats native survival and
Royston-Parmar model objects as valid prediction targets when flattening
and selecting fitted models.fastml(),
train_models(), and bootstrap confidence interval
computations now restore the caller’s .Random.seed after
execution.folds, flatten_and_rename_models(), and
get_best_model_idx() for clearer usage and cleaner package
checks.event_class validation in both
fastml() and train_models() so invalid values
are rejected consistently.logistic_reg is converted
to multinom_reg before the training loop, avoiding
per-iteration mutation and preserving engine parameter transfer.parsnip::discrim_linear() and
parsnip::discrim_quad(), resolving dependency warnings
caused by referencing unexported discrim objects.survreg and
royston_parmar..GlobalEnv inside sandboxed preprocessing guards, resolving
the corresponding R CMD check NOTE about global environment
assignments.save.fastml() in favour of
save_fastml() to avoid confusion with a non-generic
S3-style naming pattern.flatten_and_rename_models() and
get_best_model_idx(), resolving R CMD check
\usage warnings.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 parameter to fastml() to
optionally store models trained on each CV fold, enabling stability
analysis with explain_stability().print.fastml_stability() and
plot.fastml_stability() methods for convenient display of
stability analysis results.fastml_prepare_explainer_inputs() helper function providing
consistent data preparation across all explainer methods
(explain_dalex(), explain_ale(),
plot_ice(), interaction_strength(),
surrogate_tree()).resolve_positive_class() helper for consistent positive
class handling across explainer functions, respecting
event_class settings.explain_dalex(): Major
refactoring with robust preprocessing (“baking”) helper that handles
three scenarios: no preprocessor, successful baking, and fallback
validation for already-processed data.plot_ice(): Added
target_class parameter for classification, improved feature
validation with informative error messages, and added warnings for
multiclass problems.valid_model() helper to properly validate workflow and
native survival model types during prediction.predict.fastml() to
correctly resolve base algorithm names to their full “algorithm
(engine)” format.Rplots.pdf files from being created during
test execution by adding graphics device suppression to plotting
tests.Rplots.pdf to .gitignore to prevent
accidental tracking.engine_params argument to allow passing engine-specific
options in a consistent way.fastml generics.multiclass_auc to use macro_weighted
class-prevalence weighting.survival_metric_convention to align survival
evaluation defaults with tidymodels conventions when desired.NA survival predictions and
early exits during survival time computation.
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.