diff --git a/himalaya/kernel_ridge/_kernelizer.py b/himalaya/kernel_ridge/_kernelizer.py
index 9ed75f2..31bf08d 100644
--- a/himalaya/kernel_ridge/_kernelizer.py
+++ b/himalaya/kernel_ridge/_kernelizer.py
@@ -245,6 +245,11 @@ class ColumnKernelizer(ColumnTransformer):
         If True, computations will be performed on CPU, ignoring the
         current backend. If False, use the current backend.
 
+    force_int_remainder_cols : bool, default=True
+        Force the columns of the last entry of transformers_, which corresponds 
+        to the “remainder” transformer, to always be stored as indices (int) 
+        rather than column names (str). 
+
     Attributes
     ----------
     transformers_ : list
@@ -308,7 +313,8 @@ class ColumnKernelizer(ColumnTransformer):
     kernelizer = False
 
     def __init__(self, transformers, remainder='drop', n_jobs=None,
-                 transformer_weights=None, verbose=False, force_cpu=False):
+                 transformer_weights=None, verbose=False, force_cpu=False,
+                 force_int_remainder_cols=True):
         self.transformers = transformers
         self.remainder = remainder
         self.sparse_threshold = 0
@@ -316,6 +322,7 @@ def __init__(self, transformers, remainder='drop', n_jobs=None,
         self.transformer_weights = transformer_weights
         self.verbose = verbose
         self.force_cpu = force_cpu
+        self.force_int_remainder_cols = force_int_remainder_cols
 
     def _iter(self, fitted=False, *args, **kwargs):
         """
diff --git a/himalaya/kernel_ridge/_random_search.py b/himalaya/kernel_ridge/_random_search.py
index 6e75a8e..908ff9c 100644
--- a/himalaya/kernel_ridge/_random_search.py
+++ b/himalaya/kernel_ridge/_random_search.py
@@ -195,7 +195,7 @@ def solve_multiple_kernel_ridge_random_search(
         warnings.warn("Skip fit because HIMALAYA_SKIP_FIT=True.")
         # skip the loop by emptying the gammas candidates
         gammas = gammas[:0]
-        # fill with fake weights, to avoid gettings only zeros.
+        # fill with fake weights, to avoid getting only zeros.
         refit_weights += (backend.arange(n_targets)[None] + 1) / n_targets
 
     ###########################################################################