diff --git a/flaml/automl.py b/flaml/automl.py
index c14772c06..da0c59458 100644
--- a/flaml/automl.py
+++ b/flaml/automl.py
@@ -678,6 +678,7 @@ class AutoML(BaseEstimator):
              }
          }
         ```
+            skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
             fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
                 e.g.,
 
@@ -732,6 +733,7 @@ class AutoML(BaseEstimator):
             "fit_kwargs_by_estimator", {}
         )
         settings["custom_hp"] = settings.get("custom_hp", {})
+        settings["skip_transform"] = settings.get("skip_transform", False)
 
         self._estimator_type = (
             "classifier" if settings["task"] in CLASSIFICATION else "regressor"
@@ -1119,7 +1121,7 @@ class AutoML(BaseEstimator):
                 "or all columns of X are integer ids (tokenized)"
             )
 
-        if issparse(X_train_all):
+        if issparse(X_train_all) or self._skip_transform:
             self._transformer = self._label_transformer = False
             self._X_train_all, self._y_train_all = X, y
         else:
@@ -1540,6 +1542,7 @@ class AutoML(BaseEstimator):
         record_id=-1,
         auto_augment=None,
         custom_hp=None,
+        skip_transform=None,
         fit_kwargs_by_estimator=None,
         **fit_kwargs,
     ):
@@ -1649,6 +1652,7 @@ class AutoML(BaseEstimator):
 
         self._state.fit_kwargs = fit_kwargs
         self._state.custom_hp = custom_hp or self._settings.get("custom_hp")
+        self._skip_transform = self._settings.get("skip_transform") if skip_transform is None else skip_transform
         self._state.fit_kwargs_by_estimator = (
             fit_kwargs_by_estimator or self._settings.get("fit_kwargs_by_estimator")
         )
@@ -2070,6 +2074,7 @@ class AutoML(BaseEstimator):
         use_ray=None,
         metric_constraints=None,
         custom_hp=None,
+        skip_transform=None,
         fit_kwargs_by_estimator=None,
         **fit_kwargs,
     ):
@@ -2274,6 +2279,8 @@ class AutoML(BaseEstimator):
                 Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the
                 domain of the custom search space can either be a value of a sample.Domain object.
 
+
+
         ```python
         custom_hp = {
             "transformer_ms": {
@@ -2287,6 +2294,7 @@ class AutoML(BaseEstimator):
         }
         ```
 
+        skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
         fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
                 For TransformersEstimator, available fit_kwargs can be found from
                 [TrainingArgumentsForAuto](nlp/huggingface/training_args).
@@ -2418,6 +2426,7 @@ class AutoML(BaseEstimator):
 
         self._state.fit_kwargs = fit_kwargs
         custom_hp = custom_hp or self._settings.get("custom_hp")
+        self._skip_transform = self._settings.get("skip_transform") if skip_transform is None else skip_transform
         fit_kwargs_by_estimator = fit_kwargs_by_estimator or self._settings.get(
             "fit_kwargs_by_estimator"
         )
diff --git a/test/automl/test_classification.py b/test/automl/test_classification.py
index 8805fcfba..3ab6c8de0 100644
--- a/test/automl/test_classification.py
+++ b/test/automl/test_classification.py
@@ -155,6 +155,25 @@ class TestClassification(unittest.TestCase):
             # "verbose": 4,
             "ensemble": True,
         }
+        automl_settings["keep_search_state"] = True
+        automl.fit(X, y, **automl_settings)
+        X, y = automl._X_train_all, automl._y_train_all
+        del automl
+
+        automl = AutoML()
+        automl_settings = {
+            "time_budget": 3,
+            "task": "classification",
+            "n_jobs": 1,
+            "estimator_list": ["kneighbor"],
+            "eval_method": "cv",
+            "n_splits": 3,
+            "metric": "accuracy",
+            "log_training_metric": True,
+            # "verbose": 4,
+            "ensemble": True,
+            "skip_transform": True,
+        }
         automl.fit(X, y, **automl_settings)
         del automl