diff --git a/amlb/datautils.py b/amlb/datautils.py
index 94aab0981..1d7dc42fb 100644
--- a/amlb/datautils.py
+++ b/amlb/datautils.py
@@ -325,7 +325,9 @@ def impute_array(
         return imputed
 
 
-def impute_dataframe(X_fit: pd.DataFrame, *X_s: pd.DataFrame, missing_values=np.NaN, strategy='mean'):
+def impute_dataframe(X_fit: pd.DataFrame, *X_s: Iterable[pd.DataFrame], missing_values: Any=np.NaN,
+                     strategy: Literal['mean','median','mode'] | Tuple[Literal['constant'], Any] ='mean'
+                    ) -> pd.DataFrame | list[pd.DataFrame]:
     """
     :param X_fit: used to fit the imputer. This dataframe is also imputed.
     :param X_s: the additional (optional) dataframe that are imputed using the same imputer.
@@ -349,29 +351,35 @@ def impute_dataframe(X_fit: pd.DataFrame, *X_s: pd.DataFrame, missing_values=np.
     return imputed if X_s else imputed[0]
 
 
-def _impute_pd(X_fit, *X_s, missing_values=np.NaN, strategy=None, is_int=False):
+def _impute_pd(
+        X_fit: pd.DataFrame,
+        *X_s: Iterable[pd.DataFrame],
+        missing_values: Any = np.NaN,
+        strategy: Literal['mean','median','mode'] | Tuple[Literal['constant'], Any] | None =None,
+        is_int: bool = False
+) -> list[pd.DataFrame]:
     if strategy == 'mean':
         fill = X_fit.mean()
     elif strategy == 'median':
         fill = X_fit.median()
     elif strategy == 'mode':
-        fill = X_fit.mode().iloc[0, :]
+        fill = X_fit.mode().iloc[0, :]  # type: ignore[call-overload]
     elif isinstance(strategy, tuple) and strategy[0] == 'constant':
         fill = strategy[1]
     else:
-        return [X_fit, *X_s]
+        return [X_fit, *X_s]  # type: ignore[list-item]  # doesn't seem to understand unpacking
 
     if is_int and isinstance(fill, pd.Series):
         fill = fill.round()
     return [df.replace(missing_values, fill) for df in [X_fit, *X_s]]
 
 
-def _rows_with_nas(X):
+def _rows_with_nas(X: np.ndarray | pd.DataFrame) -> pd.DataFrame:
     df = X if isinstance(X, pd.DataFrame) else pd.DataFrame(X)
     return df[df.isna().any(axis=1)]
 
 
-def _restore_dtypes(X_np, X_ori):
+def _restore_dtypes(X_np: np.ndarray, X_ori: pd.DataFrame | pd.Series | np.ndarray) -> pd.DataFrame | pd.Series |  np.ndarray:
     if isinstance(X_ori, pd.DataFrame):
         df = pd.DataFrame(X_np, columns=X_ori.columns, index=X_ori.index).convert_dtypes()
         df.astype(X_ori.dtypes.to_dict(), copy=False, errors='raise')
diff --git a/amlb/results.py b/amlb/results.py
index 4cef9498e..b3991fac5 100644
--- a/amlb/results.py
+++ b/amlb/results.py
@@ -309,7 +309,7 @@ def save_predictions(dataset: Dataset, output_file: str,
 
         if probabilities is not None:
             prob_cols = probabilities_labels if probabilities_labels else dataset.target.label_encoder.classes
-            df = to_data_frame(probabilities, columns=prob_cols)
+            df = to_data_frame(probabilities, column_names=prob_cols)
             if probabilities_labels is not None:
                 df = df[sort(prob_cols)]  # reorder columns alphabetically: necessary to match label encoding
                 if any(prob_cols != df.columns.values):