fix: remove defaults values of classifier metrics

src/safeds/ml/classical/classification/_classifier.py

@@ -117,7 +117,7 @@ def accuracy(self, validation_or_test_set: TaggedTable) -> float:
 
         return sk_accuracy_score(expected_values._data, predicted_values._data)
 
-    def precision(self, validation_or_test_set: TaggedTable, positive_class: Any = 1) -> float:
+    def precision(self, validation_or_test_set: TaggedTable, positive_class: Any) -> float:
         """
         Compute the classifier's precision on the given data.
 
@@ -154,7 +154,7 @@ def precision(self, validation_or_test_set: TaggedTable, positive_class: Any = 1
             return 1.0
         return n_true_positives / (n_true_positives + n_false_positives)
 
-    def recall(self, validation_or_test_set: TaggedTable, positive_class: Any = 1) -> float:
+    def recall(self, validation_or_test_set: TaggedTable, positive_class: Any) -> float:
         """
         Compute the classifier's recall on the given data.
 
@@ -191,7 +191,7 @@ def recall(self, validation_or_test_set: TaggedTable, positive_class: Any = 1) -
             return 1.0
         return n_true_positives / (n_true_positives + n_false_negatives)
 
-    def f1_score(self, validation_or_test_set: TaggedTable, positive_class: Any = 1) -> float:
+    def f1_score(self, validation_or_test_set: TaggedTable, positive_class: Any) -> float:
         """
         Compute the classifier's $F_1$-score on the given data.