Package: tidyAML 0.0.6.9000
tidyAML: Automatic Machine Learning with 'tidymodels'
The goal of this package will be to provide a simple interface for automatic machine learning that fits the 'tidymodels' framework. The intention is to work for regression and classification problems with a simple verb framework.
Authors:
tidyAML_0.0.6.9000.tar.gz
tidyAML_0.0.6.9000.zip(r-4.7)tidyAML_0.0.6.9000.zip(r-4.6)tidyAML_0.0.6.9000.zip(r-4.5)
tidyAML_0.0.6.9000.tgz(r-4.6-any)tidyAML_0.0.6.9000.tgz(r-4.5-any)
tidyAML_0.0.6.9000.tar.gz(r-4.7-any)tidyAML_0.0.6.9000.tar.gz(r-4.6-any)
tidyAML_0.0.6.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
tidyAML/json (API)
NEWS
| # Install 'tidyAML' in R: |
| install.packages('tidyAML', repos = c('https://spsanderson.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/spsanderson/tidyaml/issues
Pkgdown/docs site:https://www.spsanderson.com
automatic-machine-learningautomlclassificationmachine-learningparsnipr-languager-programmingregressiontidytidymodelstidyverse
Last updated from:4a403eb001. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 226 | ||
| source / vignettes | OK | 284 | ||
| linux-release-x86_64 | OK | 215 | ||
| macos-release-arm64 | OK | 95 | ||
| macos-oldrel-arm64 | OK | 133 | ||
| windows-devel | OK | 147 | ||
| windows-release | OK | 131 | ||
| windows-oldrel | OK | 136 | ||
| wasm-release | OK | 188 |
Exports::=.data%>%as_labelas_namebrulee_classification_logistic_regbrulee_classification_mlpbrulee_classification_multinom_regbrulee_regression_linear_regbrulee_regression_mlpcheck_duplicate_rowscore_packagescreate_model_speccreate_splitscreate_workflow_setcubist_regression_cubist_rulesdbarts_classification_bartdbarts_regression_bartearth_classification_bag_marsearth_classification_discrim_flexibleearth_classification_marsearth_regression_bag_marsearth_regression_marsenquoenquosextract_model_specextract_regression_residualsextract_tunable_paramsextract_wflwextract_wflw_fitextract_wflw_predfast_classificationfast_classification_parsnip_spec_tblfast_regressionfast_regression_parsnip_spec_tblfull_internal_make_wflwgee_classification_logistic_reggee_regression_linear_reggee_regression_poisson_regget_modelglm_classification_logistic_regglm_regression_linear_regglm_regression_poisson_regglmer_classification_logistic_regglmer_regression_linear_regglmer_regression_poisson_regglmnet_classification_logistic_regglmnet_classification_multinom_regglmnet_regression_linear_regglmnet_regression_poisson_reggls_regression_linear_reghurdle_regression_poisson_reginstall_depsinternal_make_fitted_wflwinternal_make_spec_tblinternal_make_wflwinternal_make_wflw_gee_lin_reginternal_make_wflw_predictionsinternal_set_args_to_tunekernlab_classification_svm_linearkernlab_classification_svm_polykernlab_classification_svm_rbfkernlab_regression_svm_linearkernlab_regression_svm_polykernlab_regression_svm_rbfkknn_classification_nearest_neighborkknn_regression_nearest_neighborklar_classification_discrim_regularizedklar_classification_naive_bayesliblinear_classification_logistic_regliblinear_classification_svm_linearliblinear_regression_svm_linearlightgbm_regression_boost_treeliquidsvm_classification_svm_rbflm_regression_linear_reglme_regression_linear_reglmer_regression_linear_regload_depsmake_classification_base_tblmake_regression_base_tblmass_classification_discrim_linearmass_classification_discrim_quadmatch_argsmda_classification_discrim_linearmgcv_classification_gen_additive_modmgcv_regression_gen_additive_modnnet_classification_mlpnnet_classification_multinom_regnnet_regression_mlppartykit_regression_decision_treeplot_regression_predictionsplot_regression_residualsquantile_normalizerandomforest_regression_rand_forestranger_regression_rand_forestrpart_regression_bag_treerpart_regression_decision_treesda_classification_discrim_linearsparsediscrim_classification_discrim_linearsparsediscrim_classification_discrim_quadstan_glmer_regression_linear_regstan_glmer_regression_poisson_regstan_regression_linear_regstan_regression_poisson_regxgboost_regression_boost_treexrf_classification_rule_fitxrf_regression_rule_fitzeroinfl_regression_poisson_reg
Dependencies:backportsbase64encbroombslibcachemclasscliclockcodetoolscpp11data.tablediagramdialsDiceDesigndigestdplyrevaluatefarverfastmapfontawesomeforcatsfsfurrrfuturefuture.applyGauProgenericsggplot2globalsgluegowergtablehardhathighrhtmltoolsipredisobandjquerylibjsonliteKernSmoothknitrlabelinglatticelavalbfgslifecyclelistenvlubridatemagrittrMASSMatrixmemoisemimemixoptmodelenvnnetnumDerivparallellyparsnippillarpkgconfigprettyunitsprodlimprogressrpurrrR6rappdirsRColorBrewerRcppRcppArmadillorecipesrlangrmarkdownrpartrsampleS7sassscalessfdshapeslidersparsevctrssplitfngrSQUAREMstringistringrsurvivaltailortibbletidyrtidyselecttimechangetimeDatetinytextunetzdbutf8vctrsviridisLitewarpwithrworkflowsworkflowsetsxfunyamlyardstick
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Check for Duplicate Rows in a Data Frame | check_duplicate_rows |
| Functions to Install all Core Libraries | core_packages |
| Generate Model Specification calls to 'parsnip' | create_model_spec |
| Utility Create Splits Object | create_splits |
| Create a Workflow Set Object | create_workflow_set |
| Extract A Model Specification | extract_model_spec |
| Extract Residuals from Fast Regression Models | extract_regression_residuals |
| Extract Tunable Parameters from Model Specifications | extract_tunable_params |
| Extract A Model Workflow | extract_wflw |
| Extract A Model Fitted Workflow | extract_wflw_fit |
| Extract A Model Workflow Predictions | extract_wflw_pred |
| Generate Model Specification calls to 'parsnip' | fast_classification |
| Utility Classification call to 'parsnip' | fast_classification_parsnip_spec_tbl |
| Generate Model Specification calls to 'parsnip' | fast_regression |
| Utility Regression call to 'parsnip' | fast_regression_parsnip_spec_tbl |
| Full Internal Workflow for Model and Recipe | full_internal_make_wflw |
| Get a Model | get_model |
| Functions to Install all Core Libraries | install_deps |
| Internals Safely Make a Fitted Workflow from Model Spec tibble | internal_make_fitted_wflw |
| Internals Make a Model Spec tibble | internal_make_spec_tbl |
| Internals Safely Make Workflow from Model Spec tibble | internal_make_wflw |
| Internals Safely Make Workflow for GEE Linear Regression | internal_make_wflw_gee_lin_reg |
| Internals Safely Make Predictions on a Fitted Workflow from Model Spec tibble | internal_make_wflw_predictions |
| Internal Model Builders for Classification | brulee_classification_logistic_reg brulee_classification_mlp brulee_classification_multinom_reg dbarts_classification_bart earth_classification_bag_mars earth_classification_discrim_flexible earth_classification_mars gee_classification_logistic_reg glmer_classification_logistic_reg glmnet_classification_logistic_reg glmnet_classification_multinom_reg glm_classification_logistic_reg internal_model_builders_classification kernlab_classification_svm_linear kernlab_classification_svm_poly kernlab_classification_svm_rbf kknn_classification_nearest_neighbor klar_classification_discrim_regularized klar_classification_naive_bayes liblinear_classification_logistic_reg liblinear_classification_svm_linear liquidsvm_classification_svm_rbf mass_classification_discrim_linear mass_classification_discrim_quad mda_classification_discrim_linear mgcv_classification_gen_additive_mod nnet_classification_mlp nnet_classification_multinom_reg sda_classification_discrim_linear sparsediscrim_classification_discrim_linear sparsediscrim_classification_discrim_quad xrf_classification_rule_fit |
| Internal Model Builders for Regression | brulee_regression_linear_reg brulee_regression_mlp cubist_regression_cubist_rules dbarts_regression_bart earth_regression_bag_mars earth_regression_mars gee_regression_linear_reg gee_regression_poisson_reg glmer_regression_linear_reg glmer_regression_poisson_reg glmnet_regression_linear_reg glmnet_regression_poisson_reg glm_regression_linear_reg glm_regression_poisson_reg gls_regression_linear_reg hurdle_regression_poisson_reg internal_model_builders_regression kernlab_regression_svm_linear kernlab_regression_svm_poly kernlab_regression_svm_rbf kknn_regression_nearest_neighbor liblinear_regression_svm_linear lightgbm_regression_boost_tree lmer_regression_linear_reg lme_regression_linear_reg lm_regression_linear_reg mgcv_regression_gen_additive_mod nnet_regression_mlp partykit_regression_decision_tree randomforest_regression_rand_forest ranger_regression_rand_forest rpart_regression_bag_tree rpart_regression_decision_tree stan_glmer_regression_linear_reg stan_glmer_regression_poisson_reg stan_regression_linear_reg stan_regression_poisson_reg xgboost_regression_boost_tree xrf_regression_rule_fit zeroinfl_regression_poisson_reg |
| Internals Make a Tunable Model Specification | internal_set_args_to_tune |
| Functions to Install all Core Libraries | load_deps |
| Internals Make Base Classification Tibble | make_classification_base_tbl |
| Internals Make Base Regression Tibble | make_regression_base_tbl |
| Match function arguments | match_args |
| Create ggplot2 plot of regression predictions | plot_regression_predictions |
| Create ggplot2 plot of regression residuals | plot_regression_residuals |
| Perform quantile normalization on a numeric matrix/data.frame | quantile_normalize |
