diff --git a/ML/2-modeling.R b/ML/2-modeling.R
index 7159f27..0d27fbc 100644
--- a/ML/2-modeling.R
+++ b/ML/2-modeling.R
@@ -28,8 +28,6 @@ set.seed(070823) #set seed for reproducible research
 model_data <- readr$read_rds(here("ML","data-unshared","model_data.RDS"))
 
 
-
-
 # split data --------------------------------------------------------------
 
 model_data_split <- rsample$initial_split(
@@ -46,37 +44,44 @@ table(ds_train$ft4_dia) %>% prop.table()
 table(ds_test$ft4_dia) %>% prop.table()
 
 
+class_train <- ds_train %>% dplyr::select(-FT4)  # training data for classification models
+reg_train <- ds_train %>% dplyr::select(-ft4_dia) # training data for reg models predicting result
 
 # random forest -----------------------------------------------------------
 
+# base model - No Hyper Tuning
 
 rf_model <- p$rand_forest(trees = 1900) %>%
-  p$set_engine("ranger") %>% p$set_mode("regression")
+  p$set_engine("ranger") %>% p$set_mode("classification")
 
-rf_recipe <- r$recipe(FT4 ~ . , data = ds_train) %>%
+rf_recipe <- r$recipe(ft4_dia ~ . , data = class_train) %>%
   r$update_role(subject_id, new_role = "id") %>%
   r$update_role(charttime, new_role = "time") %>%
-  r$update_role(ft4_dia, new_role = "class") %>%
   r$step_impute_bag(r$all_predictors())
 
 
-
 rf_workflow <- wf$workflow() %>%
   wf$add_model(rf_model) %>%
   wf$add_recipe(rf_recipe)
 
-rf_fit <- p$fit(rf_workflow, ds_train)
+rf_fit <- p$fit(rf_workflow, class_train)
 
-rf_predict <- ds_train %>%
-  dplyr::select(FT4) %>%
-  dplyr::bind_cols(predict(rf_fit, ds_train))
+rf_predict <- class_train %>%
+  dplyr::select(ft4_dia) %>%
+  dplyr::bind_cols(
+    predict(rf_fit, class_train)
+    ,predict(rf_fit, class_train, type = "prob")
+    )
 
+conf_mat_rf <- ys$conf_mat(rf_predict, ft4_dia, .pred_class)
 
-gp2$ggplot(rf_predict, gp2$aes(x = FT4, y = .pred)) +
-  gp2$geom_point()
+# explainer_rf <- DALEXtra::explain_tidymodels(
+#   rf_fit
+#   ,data = class_train
+#   ,y = class_train$ft4_dia
+# )
 
-ys$rmse(rf_predict, FT4, .pred)
+# this takes awhile to run
+#vip_lm <- DALEX::model_parts(explainer_rf)
 
-metrics <- ys$metric_set(ys$rmse, ys$rsq, ys$mae)
-
-metrics(rf_predict, FT4, .pred)
+#plot(vip_lm)