Built site for AMR@3.0.1.9047: e0f8cf0

reference/amr-tidymodels.md

@@ -106,7 +106,6 @@ preparing data for modelling, especially with classification models.
 
 ``` r
 if (require("tidymodels")) {
-
   # The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
   # Presence of ESBL genes was predicted based on raw MIC values.
 
@@ -125,13 +124,10 @@ if (require("tidymodels")) {
 
   # Create and prep a recipe with MIC log2 transformation
   mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
-
     # Optionally remove non-predictive variables
     remove_role(genus, old_role = "predictor") %>%
-
     # Apply the log2 transformation to all MIC predictors
     step_mic_log2(all_mic_predictors()) %>%
-
     # And apply the preparation steps
     prep()
 
@@ -152,13 +148,15 @@ if (require("tidymodels")) {
     bind_cols(out_testing)
 
   # Evaluate predictions using standard classification metrics
-  our_metrics <- metric_set(accuracy,
-                            recall,
-                            precision,
-                            sensitivity,
-                            specificity,
-                            ppv,
-                            npv)
+  our_metrics <- metric_set(
+    accuracy,
+    recall,
+    precision,
+    sensitivity,
+    specificity,
+    ppv,
+    npv
+  )
   metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
 
   # Show performance