diff --git a/src/triage/component/postmodeling/model_analyzer.py b/src/triage/component/postmodeling/model_analyzer.py
index 361e05d29..1fbb2bd3b 100644
--- a/src/triage/component/postmodeling/model_analyzer.py
+++ b/src/triage/component/postmodeling/model_analyzer.py
@@ -770,7 +770,7 @@ def plot_precision_threshold_curve(self, ax, matrix_uuid=None):
         return ax
 
     # TODO: Facilitate plotting pr-k with absolute thresholds
-    def plot_precision_recall_curve(self, ax=None, matrix_uuid=None, list_size_upper_bound_pct=1, pct_step_size=0.01, subset_hash=None):
+    def plot_precision_recall_curve(self, ax=None, matrix_uuid=None, list_size_upper_bound_pct=1, pct_step_size=0.01, subset_hash=None, title_string=None):
         """
         Plots precision-recall curves at each train_end_time for all model groups
         
@@ -833,7 +833,7 @@ def plot_precision_recall_curve(self, ax=None, matrix_uuid=None, list_size_upper
 
         else:
             logging.debug(f'Found saved predictions for model id {self.model_id}.(group: {self.model_group_id})')
-            k_values = np.arange(0 + pct_step_size, list_size_upper_bound_pct + pct_step_size, pct_step_size)
+            k_values = np.arange(0, list_size_upper_bound_pct + pct_step_size, pct_step_size)
 
             precisions = list()
             recalls = list()
@@ -845,21 +845,31 @@ def plot_precision_recall_curve(self, ax=None, matrix_uuid=None, list_size_upper
 
                 pred_pos = pred_df.iloc[:num_above_thresh]
                 
-                precision = pred_pos.label_value.sum() / num_above_thresh
-                recall = pred_pos.label_value.sum() / num_positives
+                
+                precision = pred_pos.label_value.sum() / num_above_thresh if num_above_thresh > 0 else 0
+                recall = pred_pos.label_value.sum() / num_positives if num_above_thresh > 0 else 0
 
                 precisions.append(precision)
                 recalls.append(recall)
-            sns.lineplot(x=k_values * 100, y=precisions, ax=ax, label='Precision@k')
-            sns.lineplot(x=k_values * 100, y=recalls, ax=ax, label='Recall@k')
+                
+            with sns.axes_style("darkgrid"): 
+                sns.lineplot(x=k_values * 100, y=precisions, ax=ax, label='Precision@k')
+                sns.lineplot(x=k_values * 100, y=recalls, ax=ax, label='Recall@k')
             
 
         ax.set_xlabel('Population percentage (k %)')
         ax.set_ylabel('Metric Value')
+        ax.set_xlim(0, 100)
+        ax.set_ylim(0, 1)
         # ax.set_title(f'Model {self.model_id}, group: {self.model_group_id}')
         # ax.set_title('Precision-Recall Curve')
-        ax.set_title(f'Model: {self.model_id}, Group: {self.model_group_id}')
-
+        
+        ax.legend(frameon=False)
+        
+        if title_string is None:
+            ax.set_title(f'Model: {self.model_id}, Group: {self.model_group_id}')
+        else:
+            ax.set_title(title_string)
         return ax
 
     def plot_feature_importance(self, ax, n_top_features=20):