Package: stacks 1.0.5
Simon Couch
stacks: Tidy Model Stacking
Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking.
Authors:
stacks_1.0.5.tar.gz
stacks_1.0.5.tar.gz(r-4.5-noble)stacks_1.0.5.tar.gz(r-4.4-noble)
stacks_1.0.5.tgz(r-4.4-emscripten)stacks_1.0.5.tgz(r-4.3-emscripten)
stacks.pdf |stacks.html✨
stacks/json (API)
NEWS
| # Install 'stacks' in R: |
| install.packages('stacks', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tidymodels/stacks/issues
- class_folds - Example Objects
- class_res_nn - Example Objects
- class_res_rf - Example Objects
- log_res_nn - Example Objects
- log_res_rf - Example Objects
- reg_folds - Example Objects
- reg_res_lr - Example Objects
- reg_res_sp - Example Objects
- reg_res_svm - Example Objects
- tree_frogs - Tree frog embryo hatching data
- tree_frogs_class_test - Example Objects
- tree_frogs_reg_test - Example Objects
Last updated 2 months agofrom:b847d20d50. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 22 2024 |
| R-4.5-linux | OK | Aug 22 2024 |
Exports:%>%add_candidatesaugmentautoplotaxe_callaxe_call.model_stackaxe_ctrlaxe_ctrl.model_stackaxe_dataaxe_data.model_stackaxe_envaxe_env.model_stackaxe_fittedaxe_fitted.model_stackblend_predictionsbuild_linear_predictorbutchercollect_parameterscontrol_stack_bayescontrol_stack_gridcontrol_stack_resamplesfit_membersget_expressionspredict.data_stackpredict.model_stackprediction_eqnstack_predictstacks
Dependencies:butcherclasscliclockcodetoolscolorspacecpp11crayondata.tablediagramdialsDiceDesigndigestdoFuturedplyrfansifarverforeachfurrrfuturefuture.applygenericsggplot2glmnetglobalsgluegowerGPfitgtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalhslifecyclelistenvlobstrlubridatemagrittrMASSMatrixmgcvmodelenvmunsellnlmennetnumDerivparallellyparsnippillarpkgconfigprettyunitsprodlimprogressrpurrrR6RColorBrewerRcppRcppEigenrecipesrlangrpartrsamplescalessfdshapesliderSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetunetzdbutf8vctrsviridisLitewarpwithrworkflowsyardstick
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Add model definitions to a data stack | add_candidates |
| Augment a model stack | augment.model_stack |
| Plot results of a stacked ensemble model. | autoplot.linear_stack |
| Axing a model_stack. | axe_call.model_stack axe_ctrl.model_stack axe_data.model_stack axe_env.model_stack axe_fitted.model_stack axe_model_stack |
| Determine stacking coefficients from a data stack | blend_predictions |
| Collect candidate parameters and stacking coefficients | collect_parameters collect_parameters.data_stack collect_parameters.default collect_parameters.model_stack |
| Control wrappers | control_stack_bayes control_stack_grid control_stack_resamples |
| Example Objects | class_folds class_res_nn class_res_rf example_data log_res_nn log_res_rf reg_folds reg_res_lr reg_res_sp reg_res_svm tree_frogs_class_test tree_frogs_reg_test |
| Fit model stack members with non-zero stacking coefficients | fit_members |
| Obtain prediction equations for all possible values of type | get_expressions get_expressions._elnet get_expressions._lognet get_expressions._multnet |
| Predicting with a model stack | predict.data_stack |
| Predicting with a model stack | predict.model_stack |
| Initialize a Stack | stacks |
| stacks: Tidy Model Stacking | stacks-package stacks_description stacks_package |
| Tree frog embryo hatching data | tree_frogs |
