Package: tune 2.1.0.9000
Max Kuhn
tune: Tidy Tuning Tools
The ability to tune models is important. 'tune' contains functions and classes to be used in conjunction with other 'tidymodels' packages for finding reasonable values of hyper-parameters in models, preprocessing methods, and post-processing steps.
Authors:
tune_2.1.0.9000.tar.gz
tune_2.1.0.9000.zip(r-4.7)tune_2.1.0.9000.zip(r-4.6)tune_2.1.0.9000.zip(r-4.5)
tune_2.1.0.9000.tgz(r-4.6-any)tune_2.1.0.9000.tgz(r-4.5-any)
tune_2.1.0.9000.tar.gz(r-4.7-any)tune_2.1.0.9000.tar.gz(r-4.6-any)
tune_2.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
tune/json (API)
NEWS
| # Install 'tune' in R: |
| install.packages('tune', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tidymodels/tune/issues
Pkgdown/docs site:https://tune.tidymodels.org
- ames_grid_search - Example Analysis of Ames Housing Data
- ames_iter_search - Example Analysis of Ames Housing Data
- ames_wflow - Example Analysis of Ames Housing Data
Last updated from:db9702f2a1. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 218 | ||
| source / vignettes | OK | 216 | ||
| linux-release-x86_64 | OK | 188 | ||
| macos-release-arm64 | OK | 126 | ||
| macos-oldrel-arm64 | OK | 140 | ||
| windows-devel | OK | 208 | ||
| windows-release | OK | 200 | ||
| windows-oldrel | OK | 144 | ||
| wasm-release | OK | 211 |
Exports:.catch_and_log.check_grid.check_param_objects.config_key_from_metrics.create_weight_mapping.determine_pred_types.effective_sample_size.estimate_metrics.filter_perf_metrics.get_config_key.get_data_subsets.get_extra_col_names.get_fingerprint.get_resample_weights.get_tune_eval_time_target.get_tune_eval_times.get_tune_metric_names.get_tune_metrics.get_tune_outcome_names.get_tune_parameter_names.get_tune_parameters.get_tune_workflow.has_preprocessor.has_preprocessor_formula.has_preprocessor_recipe.has_preprocessor_variables.has_spec.is_cataclysmic.load_namespace.loop_over_all_stages.loop_over_all_stages2.make_static.needs_finalization.par_fns.set_workflow.set_workflow_recipe.set_workflow_spec.stash_last_result.update_model.update_parallel_over.update_recipe.use_case_weights_with_yardstick.validate_resample_weights.weighted_sdadd_resample_weightsaugmentautoplotcalculate_resample_weightscheck_eval_time_argcheck_initialcheck_metric_in_tune_resultscheck_metricscheck_metrics_argcheck_parameterscheck_rsetcheck_timecheck_workflowchoose_eval_timechoose_frameworkchoose_metriccollect_extractscollect_metricscollect_notescollect_predictionscompute_metricsconf_boundconf_mat_resampledcontrol_bayescontrol_gridcontrol_last_fitcontrol_resamplescoord_obs_predempty_ellipsesencode_setestimate_tune_resultseval_miraiexp_improveexpo_decayextract_fit_engineextract_fit_parsnipextract_moldextract_parameter_set_dialsextract_preprocessorextract_recipeextract_resample_weightsextract_spec_parsnipextract_workflowfilter_parametersfinalize_modelfinalize_recipefinalize_tailorfinalize_workflowfinalize_workflow_preprocessorfirst_eval_timefirst_metricfit_bestfit_max_valuefit_resamplesforge_from_workflowfuture_installedget_future_workersget_metric_timeget_mirai_workersget_parallel_seedsget_tune_colorshas_non_par_pkgsinitialize_catalogint_pctlis_preprocessoris_recipeis_workflowlast_fitload_pkgsloop_callmaybe_choose_eval_timemessage_wrapmetrics_infomin_gridmin_grid.boost_treemin_grid.C5_rulesmin_grid.cubist_rulesmin_grid.linear_regmin_grid.logistic_regmin_grid.marsmin_grid.multinom_regmin_grid.nearest_neighbormin_grid.plsmin_grid.poisson_regmin_grid.proportional_hazardsmin_grid.rule_fitmirai_installednew_backend_optionsnew_bare_tibblenew_iteration_resultsoutcome_namesparametersprob_improvepull_rset_attributesrequired_pkgsschedule_gridselect_bestselect_by_one_std_errselect_by_pct_lossshow_bestshow_notestunabletunetune_argstune_bayestune_gridval_class_and_singleval_class_or_null
Dependencies:base64encbslibcachemclasscliclockcodetoolscpp11data.tablediagramdialsDiceDesigndigestdplyrevaluatefarverfastmapfontawesomefsfurrrfuturefuture.applyGauProgenericsggplot2globalsgluegowergtablehardhathighrhtmltoolsipredisobandjquerylibjsonliteKernSmoothknitrlabelinglatticelavalbfgslifecyclelistenvlubridatemagrittrMASSMatrixmemoisemimemixoptmodelenvnnetnumDerivparallellyparsnippillarpkgconfigprettyunitsprodlimprogressrpurrrR6rappdirsRColorBrewerRcppRcppArmadillorecipesrlangrmarkdownrpartrsampleS7sassscalessfdshapeslidersparsevctrssplitfngrSQUAREMstringistringrsurvivaltailortibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitewarpwithrworkflowsxfunyamlyardstick
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Save most recent results to search path | .stash_last_result |
| Determine if case weights should be passed on to yardstick | .use_case_weights_with_yardstick .use_case_weights_with_yardstick.hardhat_frequency_weights .use_case_weights_with_yardstick.hardhat_importance_weights |
| Add resample weights to an rset object | add_resample_weights |
| Augment data with holdout predictions | augment.last_fit augment.resample_results augment.tune_results |
| Plot tuning search results | autoplot.tune_results |
| Calculate resample weights from resample sizes | calculate_resample_weights |
| Obtain and format results produced by tuning functions | collect_extracts collect_extracts.tune_results collect_metrics collect_metrics.tune_results collect_notes collect_notes.tune_results collect_predictions collect_predictions.default collect_predictions.tune_results |
| Calculate and format metrics from tuning functions | compute_metrics compute_metrics.default compute_metrics.tune_results |
| Compute average confusion matrix across resamples | conf_mat_resampled |
| Control aspects of the Bayesian search process | control_bayes |
| Control aspects of the last fit process | control_last_fit |
| Use same scale for plots of observed vs predicted values | coord_obs_pred |
| Example Analysis of Ames Housing Data | ames_grid_search ames_iter_search ames_wflow example_ames_knn |
| Exponential decay function | expo_decay |
| Extract resample weights from rset or tuning objects | extract_resample_weights |
| Extract elements of 'tune' objects | extract-tune extract_fit_engine.tune_results extract_fit_parsnip.tune_results extract_mold.tune_results extract_preprocessor.tune_results extract_recipe.tune_results extract_spec_parsnip.tune_results extract_workflow.last_fit extract_workflow.tune_results |
| Remove some tuning parameter results | filter_parameters |
| Splice final parameters into objects | finalize_model finalize_recipe finalize_tailor finalize_workflow |
| Fit a model to the numerically optimal configuration | fit_best fit_best.default fit_best.tune_results |
| Fit multiple models via resampling | fit_resamples fit_resamples.model_spec fit_resamples.workflow |
| Bootstrap confidence intervals for performance metrics | int_pctl.tune_results |
| Fit the final best model to the training set and evaluate the test set | last_fit last_fit.model_spec last_fit.workflow |
| Write a message that respects the line width | message_wrap |
| Support for parallel processing in tune | parallelism |
| Acquisition function for scoring parameter combinations | conf_bound exp_improve prob_improve |
| Investigate best tuning parameters | select_best select_best.default select_best.tune_results select_by_one_std_err select_by_one_std_err.default select_by_one_std_err.tune_results select_by_pct_loss select_by_pct_loss.default select_by_pct_loss.tune_results show_best show_best.default show_best.tune_results |
| Display distinct errors from tune objects | show_notes |
| Bayesian optimization of model parameters. | tune_bayes tune_bayes.model_spec tune_bayes.workflow |
| Model tuning via grid search | tune_grid tune_grid.model_spec tune_grid.workflow |
