Tidymodel and glmnet

When the penalized generalize linear model (Lasso or Ridge) is processed in the tidymodel environment, finalizing the hyperparameter (lambda) and getting coefficients of the final model are confusing. Here is an example. This example predicts PIK3CA mutation status by gene expression data. TCGA breast cancer dataset is used. Modeling library(glmnet) library(themis) set.seed(930093) cv_splits <- rsample::vfold_cv(trainset_ahDiff, strata = PIK3CA_T) mod <- logistic_reg(penalty = tune(), mixture = tune()) %>% set_engine("glmnet") rec <- recipe(PIK3CA_T ~ .

External Project

An example of linking directly to an external project website using `external_link`.