Tidymodels and tune

tidymodels, tune
https://www.tidymodels.org/
Tidy Modeling with R
AWESOME tidymodels
*Translations of the ISLR labs into using the tidymodels set of packages
Caret for Survival Analysis?
Survival Methods in tidymodels, 2021/11/2
censored package. censored is a “parsnip-adjacent” packages with model definitions for censored regression and survival analysis models.
A Gentle Introduction to tidymodels
Julia Silge Handle class imbalance in climbing expedition data with tidymodels
Parallel processing with tune
Tutorial on tidymodels for Machine Learning
Julia Silge Lasso regression using tidymodels and #tidytuesday data for the office
- Explore the data: transmute(), mutate(), select(), count(), add_count(), filter(), select(), pivot_wider(), distinct(), pivot_longer(), separate_rows(), add_count(), filter(), distinct(), mutate(), pivot_wider(), inner_join(), janitor::clean_names(), geom_boxplot()
- Train a model: initial_split(), training(), testing(), recipe(), update_role(), step_zv(), step_normalize(), prep(), linear_reg(), set_engine(), workflow(), add_recipe(), add_model(), fit(), pull_workflow_fit(), tidy()
- Tune lasso parameters: bootstraps(), linear_reg(), set_engine(), grid_regular(), registerDoParallel(), tune_grid(), add_model(), collect_metrics(), geom_errorbar(), geom_line(), facet_wrap(), scale_x_log10(), theme(), select_best(), finalize_workflow(), add_model(), pull_workflow_fit(), geom_col(), scale_x_continuous(), last_fit(), collect_metrics()
Tidymodel and glmnet - Jun
Tidymodels: tidy machine learning in R - Rebecca Barter
Tidy Model Stacking 2021 John M. Chambers Statistical Software Award.
Julia Silge Explore art media over time in the #TidyTuesday Tate collection dataset
Julia Silge Supervised Machine Learning for Text Analysis in R is now complete,
- Online book https://smltar.com/
Intro to tidymodels with NFL data
Julia Silge Multiclass predictive modeling for #TidyTuesday NBER papers. penalty was tuned over a grid of possible penalty parameters by using tune_grid(), doParallel and select_by_one_std_err(). There is also a confusion matrix plot and ROC curves for each class.
- Build and tune a model: initial_split(), training(), test(), vfold_cv(), recipe(), multinom_reg(), set_mode(), set_engine(), workflow(), grid_regular(), tune_grid(), autoplot(), show_best()
- Choose and evaluate a final model: select_by_one_std_err(), finalize_workflow(), last_fit(), collect_metrics(), conf_mat(), collect_predictions(), roc_curve(), extract_workflow(), predict()
TidyModels or CARET – how they compare?
- Explore data: separate(), mutate(), filter(), select(), geom_point(), geom_smooth, scale_color_viridis_c(), geom_boxplot(), geom_jitter(), theme()
- Build and fit a workflow set: filter(), initial_split(), training(), testing(), vfold_cv(), recipe(), step_other(), step_dummy(), step_bs(), rand_forest(), set_mode(), set_engine(), linear_reg(), workflow_set(), registerDoParallel(), workflow_map()
- Evaluate workflow set: autoplot(), collect_metrics(), extract_workflow(), fit(), tidy(), arrange()
LASSO models in tidymodels from STAT 253: Statistical Machine Learning
- Lasso model spec: linear_reg(), set_args(), set_engine(), set_mode()
- Recipe with standardization: recipe(), step_nzv(), step_novel(), step_normalize(), step_dummy()
- Workflow: workflow(), add_recipe(), add_model()
- Fit model:: fit()
- Examine the model for each lambda: plot( extract_fit_parsnip()) , pluck()
- Identifying the best Lasso model: vfold_cv(), grid_regular(), tune_grid()
- Visualize model evaluation metrics from tuning: autoplot(), theme_classic()
- Summarize model evaluation metrics: collect_metrics(), filter(), select()
- Fit final model: finalize_workflow(), fit(), tidy()
TidyModels hands on for the NHS-R community – building a supervised ML classification model from scratch
Closing out our year with a Q4 2021 tidymodels update
A Tidymodels Tutorial 2023/4/10
Tidymodels Machine Learning: Diabetes Classification