feat(EFDT): leaf_prediction as str

Users can still use leaf_prediction as an integer (0, 1 or 2), but it can also be used as a string:
"MajorityClass": 0, "NaiveBayes": 1, "NaiveBayesAdaptive": 2

src/capymoa/classifier/_efdt.py

@@ -44,10 +44,6 @@ class EFDT(MOAClassifier):
     84.39999999999999
     """
 
-    MAJORITY_CLASS = 0
-    NAIVE_BAYES = 1
-    NAIVE_BAYES_ADAPTIVE = 2
-
     def __init__(
         self,
         schema: Schema | None = None,
@@ -57,7 +53,7 @@ def __init__(
         split_criterion: Union[str, SplitCriterion] = "InfoGainSplitCriterion",
         confidence: float = 1e-3,
         tie_threshold: float = 0.05,
-        leaf_prediction: str = MAJORITY_CLASS,
+        leaf_prediction: str = "MajorityClass",
         nb_threshold: int = 0,
         numeric_attribute_observer: str = "GaussianNumericAttributeClassObserver",
         binary_split: bool = False,
@@ -77,8 +73,8 @@ def __init__(
         :param confidence: Significance level to calculate the Hoeffding bound. The significance level is given by
             `1 - delta`. Values closer to zero imply longer split decision delays.
         :param tie_threshold: Threshold below which a split will be forced to break ties.
-        :param leaf_prediction: Prediction mechanism used at leafs
-            (0: Majority Class, 1: Naive Bayes, 2: Naive Bayes Adaptive).
+        :param leaf_prediction: Prediction mechanism used at the leaves
+            ("MajorityClass" or 0, "NaiveBayes" or 1, "NaiveBayesAdaptive" or 2).
         :param nb_threshold: Number of instances a leaf should observe before allowing Naive Bayes.
         :param numeric_attribute_observer: The Splitter or Attribute Observer (AO) used to monitor the class statistics
             of numeric features and perform splits.
@@ -106,6 +102,9 @@ def __init__(
             "remove_poor_attrs": "-r",
             "disable_prepruning": "-p",
         }
+        if isinstance(leaf_prediction, str):
+            leaf_prediction_mapping = {"MajorityClass": 0, "NaiveBayes": 1, "NaiveBayesAdaptive": 2}
+            leaf_prediction = leaf_prediction_mapping.get(leaf_prediction, None)
 
         split_criterion = _split_criterion_to_cli_str(split_criterion)
         config_str = build_cli_str_from_mapping_and_locals(mapping, locals())