diff --git a/verticapy/machine_learning/vertica/ensemble.py b/verticapy/machine_learning/vertica/ensemble.py
index d97ba7af0..187a7062f 100755
--- a/verticapy/machine_learning/vertica/ensemble.py
+++ b/verticapy/machine_learning/vertica/ensemble.py
@@ -697,6 +697,21 @@ class RandomForestRegressor(Regressor, RandomForest):
         of the huge size of the tree. But it should render nicely
         in jupyter environment.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Model Register
     ^^^^^^^^^^^^^^
 
@@ -1503,6 +1518,21 @@ class RandomForestClassifier(MulticlassClassifier, RandomForest):
         of the huge size of the tree. But it should render nicely
         in jupyter environment.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Parameter Modification
     ^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -2153,7 +2183,7 @@ class IsolationForest(Clustering, Tree):
         exploring predictions and gaining a deeper understanding of how
         these models perform in different scenarios. Please refer to
         :ref:`chart_gallery.contour_plot` for more examples.
- 
+
     Parameter Modification
     ^^^^^^^^^^^^^^^^^^^^^^^
 
diff --git a/verticapy/machine_learning/vertica/linear_model.py b/verticapy/machine_learning/vertica/linear_model.py
index 5571830cc..9452f0a8e 100755
--- a/verticapy/machine_learning/vertica/linear_model.py
+++ b/verticapy/machine_learning/vertica/linear_model.py
@@ -324,7 +324,7 @@ class ElasticNet(Regressor, LinearModel):
         in training the model.  Note that setting
         fit_intercept  to false does  not work well with
         the BFGS optimizer.
-        
+
     Examples
     ---------
 
@@ -1026,6 +1026,21 @@ class Lasso(Regressor, LinearModel):
         The plotting feature is typically suitable for models with fewer than
         three predictors.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Parameter Modification
     ^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -1472,6 +1487,21 @@ class LinearRegression(Regressor, LinearModel):
         The plotting feature is typically suitable for models with fewer
         than three predictors.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Parameter Modification
     ^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -1920,6 +1950,21 @@ class Ridge(Regressor, LinearModel):
         The plotting feature is typically suitable for models with fewer than
         three predictors.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Parameter Modification
     ^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -2497,6 +2542,21 @@ class LogisticRegression(BinaryClassifier, LinearModelClassifier):
         The plotting feature is typically suitable for models with
         fewer than three predictors.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Parameter Modification
     ^^^^^^^^^^^^^^^^^^^^^^^
 
diff --git a/verticapy/machine_learning/vertica/svm.py b/verticapy/machine_learning/vertica/svm.py
index 188364883..92de59591 100755
--- a/verticapy/machine_learning/vertica/svm.py
+++ b/verticapy/machine_learning/vertica/svm.py
@@ -377,6 +377,21 @@ class LinearSVR(Regressor, LinearModel):
         The plotting feature is typically suitable for models with fewer than
         three predictors.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Parameter Modification
     ^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -970,6 +985,21 @@ class LinearSVC(BinaryClassifier, LinearModelClassifier):
         The plotting feature is typically suitable for models with
         fewer than three predictors.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Parameter Modification
     ^^^^^^^^^^^^^^^^^^^^^^^
 
diff --git a/verticapy/machine_learning/vertica/tree.py b/verticapy/machine_learning/vertica/tree.py
index 10d78b391..23f20d090 100755
--- a/verticapy/machine_learning/vertica/tree.py
+++ b/verticapy/machine_learning/vertica/tree.py
@@ -359,6 +359,21 @@ class DecisionTreeRegressor(RandomForestRegressor):
         of the huge size of the tree. But it should render nicely
         in jupyter environment.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Model Register
     ^^^^^^^^^^^^^^
 
@@ -738,6 +753,21 @@ class DummyTreeRegressor(RandomForestRegressor):
         of the huge size of the tree. But it should render nicely
         in jupyter environment.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Model Register
     ^^^^^^^^^^^^^^
 
@@ -1282,6 +1312,21 @@ class DecisionTreeClassifier(RandomForestClassifier):
         of the huge size of the tree. But it should render nicely
         in jupyter environment.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Parameter Modification
     ^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -1813,6 +1858,21 @@ class DummyTreeClassifier(RandomForestClassifier):
         of the huge size of the tree. But it should render nicely
         in jupyter environment.
 
+    **Contour plot** is another useful plot that can be produced
+    for models with two predictors.
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. important::
+
+    Machine learning models with two predictors can usually
+    benefit from their own contour plot. This visual representation
+    aids in exploring predictions and gaining a deeper understanding
+    of how these models perform in different scenarios.
+    Please refer to  :ref:`chart_gallery.contour` for more examples.
+
     Model Register
     ^^^^^^^^^^^^^^