diff --git a/nannyml/performance_estimation/confidence_based/metrics.py b/nannyml/performance_estimation/confidence_based/metrics.py
index 1f0f60ff..ad29f953 100644
--- a/nannyml/performance_estimation/confidence_based/metrics.py
+++ b/nannyml/performance_estimation/confidence_based/metrics.py
@@ -782,11 +782,9 @@ def estimate_f1(y_pred: Union[pd.Series, np.ndarray], y_pred_proba: Union[pd.Ser
     fp = np.where(y_pred == 1, 1 - y_pred_proba, 0)
     fn = np.where(y_pred == 0, y_pred_proba, 0)
     TP, FP, FN = np.sum(tp), np.sum(fp), np.sum(fn)
-    if TP + 0.5 * (FP + FN) == 0:
-        metric = 0
-    else:
-        metric = TP / (TP + 0.5 * (FP + FN))
-    return metric
+
+    denominator = TP + 0.5 * (FP + FN)
+    return TP / denominator if denominator != 0 else 0
 
 
 @MetricFactory.register('precision', ProblemType.CLASSIFICATION_BINARY)
@@ -929,11 +927,9 @@ def estimate_precision(y_pred: Union[pd.Series, np.ndarray], y_pred_proba: Union
     tp = np.where(y_pred == 1, y_pred_proba, 0)
     fp = np.where(y_pred == 1, 1 - y_pred_proba, 0)
     TP, FP = np.sum(tp), np.sum(fp)
-    if TP + FP == 0:
-        metric = 0
-    else:
-        metric = TP / (TP + FP)
-    return metric
+
+    denominator = TP + FP
+    return TP / denominator if denominator != 0 else 0
 
 
 @MetricFactory.register('recall', ProblemType.CLASSIFICATION_BINARY)
@@ -1076,11 +1072,9 @@ def estimate_recall(y_pred: Union[pd.Series, np.ndarray], y_pred_proba: Union[pd
     tp = np.where(y_pred == 1, y_pred_proba, 0)
     fn = np.where(y_pred == 0, y_pred_proba, 0)
     TP, FN = np.sum(tp), np.sum(fn)
-    if TP + FN == 0:
-        metric = 0
-    else:
-        metric = TP / (TP + FN)
-    return metric
+
+    denominator = TP + FN
+    return TP / denominator if denominator != 0 else 0
 
 
 @MetricFactory.register('specificity', ProblemType.CLASSIFICATION_BINARY)
@@ -1215,11 +1209,9 @@ def estimate_specificity(y_pred: Union[pd.Series, np.ndarray], y_pred_proba: Uni
     tn = np.where(y_pred == 0, 1 - y_pred_proba, 0)
     fp = np.where(y_pred == 1, 1 - y_pred_proba, 0)
     TN, FP = np.sum(tn), np.sum(fp)
-    if TN + FP == 0:
-        metric = 0
-    else:
-        metric = TN / (TN + FP)
-    return metric
+
+    denominator = TN + FP
+    return TN / denominator if denominator != 0 else 0
 
 
 @MetricFactory.register('accuracy', ProblemType.CLASSIFICATION_BINARY)