Tidymodels
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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
stacks
https://stacks.tidymodels.org/, JSM 2021 John Chambers Award Recipient
modelDown
https://modeloriented.github.io/modelDown/