reshaped mu and sigma to include a second dimension for columns insid…

…e predict_proba

skpro/regression/_dummy.py

@@ -105,13 +105,11 @@ def _predict_proba(self, X):
         """
         X_ind = X.index
         X_n_rows = X.shape[0]
-
         if self.strategy == "normal":
             # broadcast the mu and sigma from fit to the length of X
-            mu = np.ones(X_n_rows) * self._mu
-            sigma = np.ones(X_n_rows) * self._sigma
+            mu = np.reshape((np.ones(X_n_rows) * self._mu), (-1, 1))
+            sigma = np.reshape((np.ones(X_n_rows) * self._sigma), (-1, 1))
             pred_dist = Normal(mu=mu, sigma=sigma, index=X_ind, columns=self._y_columns)
-
             return pred_dist
 
         if self.strategy == "empirical":