diff --git a/src/python/nimbusml/base_predictor.py b/src/python/nimbusml/base_predictor.py
index bfa2813f..588971b3 100644
--- a/src/python/nimbusml/base_predictor.py
+++ b/src/python/nimbusml/base_predictor.py
@@ -12,7 +12,6 @@
 
 from sklearn.base import BaseEstimator
 from sklearn.utils.multiclass import unique_labels
-from sklearn.utils.validation import check_is_fitted
 
 from . import Pipeline
 from .internal.core.base_pipeline_item import BasePipelineItem
@@ -49,8 +48,6 @@ def fit(self, X, y=None, **params):
                     "Classifier can't train when only one class is "
                     "present.")
             self.classes_ = unique_classes
-        self.X_ = X
-        self.y_ = y
 
         # Clear cached summary since it should not
         # retain its value after a new call to fit
@@ -69,13 +66,24 @@ def fit(self, X, y=None, **params):
         set_shape(self, X)
         return self
 
+    @property
+    def _is_fitted(self):
+        """
+        Tells if the predictor was trained.
+        """
+        return (hasattr(self, 'model_') and
+                self.model_ and
+                os.path.isfile(self.model_))
+
     @trace
     def _invoke_inference_method(self, method, X, **params):
         """
         Returns predictions. Can be predicted labels, probabilities
         or else decision values.
         """
-        check_is_fitted(self, ["X_", "y_"])
+        if not self._is_fitted:
+            raise ValueError("Model is not fitted. "
+                             "fit() must be called before {}.".format(method))
 
         # Check that the input is of the same shape as the one passed
         # during