The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Initial CRAN release.
venn_abers() fits an Inductive Venn-Abers predictor to
calibration probabilities and labels from any binary model;
cross_venn_abers() combines the predictors of matched
cross-fitted models. predict() returns the lower and upper
Venn-Abers probabilities, a regularized probability chosen by log loss
or Brier score, and class decisions. Calibration probabilities can be
rounded with precision.brier_score(), log_loss() and
calibration_plot() assess held-out predictions.venn_abers() and cross_venn_abers() accept
factor truth and named probability matrices, with one-vs-one coupling
(default) or one-vs-rest normalization. Class diagnostics are
reconstructible from the stored binary subproblems.venn_abers_one_hot() represents existing class
decisions as a 0/1 matrix.venn_abers_regression() and
cross_venn_abers_regression() calibrate real-valued
predictions into a point with lower and upper bounds in target units,
with moderation set by m or epsilon.calibrated_workflow() trains a model and its Venn-Abers
calibrator together: Inductive or Cross, binary, shared one-vs-one or
one-vs-rest, pairwise one-vs-one and regression. Calibration splits are
created locally (calibration_prop, or independent
training_size and calibration_size), or given
explicitly through local_calibration_splits().
prediction_model = "full" refits the model on all local
rows while keeping the held-out calibrators.calibrate_workflow() adds a calibrator to an already
fitted workflow without refitting it.model_adapter() turns your own fit and predict
functions into a model for workflows. venn_abers_schema(),
venn_abers_encode() and venn_abers_decode()
keep original column names and class order.
venn_abers_details() returns bounds, fold identities and
source-model diagnostics.calibrated_workflow() specifications can be assessed by
native outer resampling with tune::fit_resamples() and
tune::last_fit().calibrated_workflow(tuning = ...) selects model, recipe
and calibration parameters by Grid or Bayes search, jointly or in two
stages, inside each local fit. venn_abers_tuning() returns
the stored search diagnostics.calibrated_workflow(postprocessing = ...) learns a
postprocessor on rows of its own before the Venn-Abers calibrator:
Sigmoid (Platt) scaling, Tailor logistic, multinomial or linear
calibration, or numeric isotonic regression, as a single holdout or an
inner ensemble.plot() methods draw the fitted calibration map or the
Venn-Abers width of manual calibrators and workflow fits.corp_plot() draws CORP reliability diagrams with
resampling bands, corp_decomposition_plot() splits the
Brier score into miscalibration, discrimination and uncertainty, and
regression_plot() shows held-out regression predictions.
All graphics use base R.browseVignettes("vennabers")).citation("vennabers") lists the package and the method
papers.help(package = "vennabers"), their arguments and defaults,
and the output formats their help pages document stay compatible
throughout 1.x. Incompatible changes wait for 2.0.0; new options keep
the previous behavior unless enabled explicitly. Documented bug fixes
may still change numerical results, and NEWS names each one.fit_vennabers(),
fit_vennabers_proxy() and
predict_vennabers_*() are exported only for model
registration; use calibrated_workflow() instead.tools/NEWS-development.md in the source repository.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.