diff --git a/core/__main__.py b/core/__main__.py
index ad98a93..cb3cd17 100644
--- a/core/__main__.py
+++ b/core/__main__.py
@@ -495,11 +495,7 @@ def _sklearn(request, context):
                 # Get labels for clustering
                 response = model.fit_transform(load_script=False)
             
-            # Set the correct data type for the response
-            if is_numeric_dtype(response):
-                dtypes = ["num"]
-            else:
-                dtypes = ["str"]
+            dtypes = ["str"]
             
         elif function in (15, 17, 28):
             if function == 15:
@@ -512,13 +508,7 @@ def _sklearn(request, context):
                 # Provide labels for clustering
                 response = model.fit_transform(load_script=True)
 
-            # Set the correct data type for the response
-            if is_numeric_dtype(response.iloc[:,2]):
-                dt = "num"
-            else:
-                dt = "str"
-            
-            dtypes = ["str", "str", dt]
+            dtypes = ["str", "str", "str"]
         
         elif function in (18, 22):
             if function == 18:
diff --git a/core/_sklearn.py b/core/_sklearn.py
index 9034cfd..a328371 100644
--- a/core/_sklearn.py
+++ b/core/_sklearn.py
@@ -1530,13 +1530,7 @@ def _send_table_description(self, variant):
         elif variant == "predict":
             self.table.fields.add(name="model_name")
             self.table.fields.add(name="key")
-            
-            # We return numerical predictions for regression and text for classification
-            if self.model.estimator_type == "regressor":
-                dt = 1
-            else:
-                dt = 0
-            self.table.fields.add(name="prediction", dataType=dt)
+            self.table.fields.add(name="prediction")
         elif variant == "expression":
             self.table.fields.add(name="result")
         elif variant == "best_params":