diff --git a/hlink/linking/model_exploration/link_step_train_test_models.py b/hlink/linking/model_exploration/link_step_train_test_models.py
index 7896142..b6fdf28 100644
--- a/hlink/linking/model_exploration/link_step_train_test_models.py
+++ b/hlink/linking/model_exploration/link_step_train_test_models.py
@@ -99,7 +99,6 @@ def _run(self) -> None:
             else:
                 threshold_ratio = False
 
-            
             # Collect auc values so we can pull out the highest
             splits_results = []
 
@@ -142,7 +141,7 @@ def _run(self) -> None:
 
                 thresholds_plus_1 = np.append(thresholds_raw, [np.nan])
                 param_text = np.full(precision.shape, f"{model_type}_{params}")
-            
+
                 if first:
                     prc = pd.DataFrame(
                         {
@@ -171,7 +170,7 @@ def _run(self) -> None:
                 "auc_mean": auc_mean,
                 "auc_standard_deviation": auc_std,
                 "model": model_type,
-                "params": params                                
+                "params": params,
             }
             print(f"PR AUC for splits on current model and params: {pr_auc_dict}")
             pr_auc_info.append(pr_auc_info)
@@ -181,7 +180,6 @@ def _run(self) -> None:
                 [probability_metrics_df, this_model_results]
             )
 
-            
             threshold_matrix = _calc_threshold_matrix(alpha_threshold, threshold_ratio)
             logger.debug(f"The threshold matrix has {len(threshold_matrix)} entries")
             results_dfs: dict[int, pd.DataFrame] = {}
@@ -219,9 +217,10 @@ def _run(self) -> None:
             )
 
             i = 0
-            for threshold_index, (this_alpha_threshold, this_threshold_ratio) in enumerate(
-                threshold_matrix, 1
-            ):
+            for threshold_index, (
+                this_alpha_threshold,
+                this_threshold_ratio,
+            ) in enumerate(threshold_matrix, 1):
                 logger.debug(
                     f"Predicting with threshold matrix entry {threshold_index} of {len(threshold_matrix)}: "
                     f"{this_alpha_threshold=} and {this_threshold_ratio=}"
@@ -256,13 +255,13 @@ def _run(self) -> None:
 
             for i in range(len(threshold_matrix)):
                 thresholded_metrics_df = _append_results(
-                    thresholded_metrics_df, results_dfs[i], pr_auc_dict["model"], pr_auc_dict["params"]
+                    thresholded_metrics_df, results_dfs[i], model_type, params
                 )
 
-        _print_thresholded_metrics_df(thresholded_metrics_df)
         thresholded_metrics_df = _load_thresholded_metrics_df_params(
             thresholded_metrics_df
         )
+        _print_thresholded_metrics_df(thresholded_metrics_df)
         self._save_training_results(thresholded_metrics_df, self.task.spark)
         self._save_otd_data(otd_data, self.task.spark)
         self.task.spark.sql("set spark.sql.shuffle.partitions=200")