dt-cart/backend/dataset/__init__.py

@@ -46,9 +46,8 @@ class Dataset:
         random_state: int,
         is_classification: bool = False,
     ) -> SplittedDataset:
-        target = params.target or data.columns[-1]
         X = data.drop([params.target], axis=1)
-        y = data[[target]]
+        y = data[[params.target]]
         stratify = None if not is_classification else y
         X_train, X_test, y_train, y_test = train_test_split(
             X,

dt-cart/backend/dataset/dto.py

@@ -8,7 +8,7 @@ class DatasetUploadDto(Schema):
 
 class DatasetDto(Schema):
     input = fields.List(fields.String(), load_default=None)
-    target = fields.String(load_default=None)
+    target = fields.String(required=True)
     sep = fields.String(load_default=",")
     decimal = fields.String(load_default=".")
     train_volume = fields.Float(load_default=0.8, validate=Range(min=0.1, max=0.9))

dt-cart/backend/tree/__init__.py

@@ -35,7 +35,7 @@ def get_rules(
                 rules.append(Rule(antecedent, value))  # type: ignore
             else:
                 index = np.argmax(tree_.value[node][0])
-                value = str(classes[index])
+                value = float(classes[index])
                 rules.append(Rule(antecedent, value))  # type: ignore
 
     recurse(0, antecedent, rules)
@@ -87,7 +87,7 @@ def get_tree(
                 nodes.append(TreeNode(parent, None, node, "result", "=", value))
             else:
                 index = np.argmax(tree_.value[node][0])
-                value = str(classes[index])
+                value = float(classes[index])
                 nodes.append(TreeNode(parent, None, node, "result", "=", value))
 
     recurse(0, None, nodes)