Refactor parameter handling for consistency

Author: seofernando25

models/cnn.py

@@ -90,14 +90,13 @@ def __init__(self, num_classes: int = 10) -> None:
         self._input_channels = 1  # grayscale CIFAR-10
 
     def create_model(self, **params) -> None:
-        kernel_size = params.get("kernel_size", 3)
-        stride = params.get("stride", 1)
-        self.params.update(
-            {
-                "kernel_size": kernel_size,
-                "stride": stride,
-            }
-        )
+        # Store all parameters passed in
+        self.params.update(params)
+        
+        # Extract architecture-specific parameters for Backbone creation
+        kernel_size = self.params.get("kernel_size", 3)
+        stride = self.params.get("stride", 1)
+        
         self.network = Backbone(
             in_channels=self._input_channels,
             num_classes=self.num_classes,

models/decision_tree.py

@@ -17,8 +17,8 @@ def __init__(self, **kwargs: Any) -> None:
 
     def create_model(self, **params: Any) -> None:
         """Create the underlying sklearn estimator."""
-        configuration = {**self.params, **params}
-        self.estimator = DecisionTreeClassifier(**configuration)
+        self.params.update(params)
+        self.estimator = DecisionTreeClassifier(**self.params)
 
     def train(self, X_train, y_train) -> DecisionTreeClassifier:
         if self.estimator is None:

models/knn.py

@@ -16,8 +16,8 @@ def __init__(self, **kwargs: Any) -> None:
         self.estimator: KNeighborsClassifier | None = None
 
     def create_model(self, **params: Any) -> None:
-        configuration = {**self.params, **params}
-        self.estimator = KNeighborsClassifier(**configuration)
+        self.params.update(params)
+        self.estimator = KNeighborsClassifier(**self.params)
 
     def train(self, X_train, y_train) -> KNeighborsClassifier:
         if self.estimator is None: