modified doc string of memmodel/linear_model.py

verticapy/machine_learning/memmodel/linear_model.py

@@ -25,15 +25,80 @@
 
 class LinearModel(InMemoryModel):
     """
-    InMemoryModel  implementation  of  linear
+    :py:mod:`verticapy.machine_learning.memmodel.base.InMemoryModel` implementation  of  linear
     algorithms.
 
+
     Parameters
     ----------
     coef: ArrayLike
         ArrayLike of the model's coefficients.
     intercept: float, optional
         The intercept or constant value.
+
+    .. note:: :py:mod:`verticapy.machine_learning.memmodel` are defined entirely by their attributes. For example, 'coefficients' and 'intercept' define a linear regression model.
+
+    Examples
+    --------
+
+    **Initalization**
+
+    Import the required module.
+
+    .. ipython:: python
+            :suppress:
+
+        from verticapy.machine_learning.memmodel.linear_model import LinearModel
+
+    A linear model is defined by its coefficients and an intercept value. In this example, we will use the following:
+
+    .. ipython:: python
+            :suppress:
+
+        coefficients = [0.5, 1.2]
+        intercept = 2.0
+
+    Let's create a :py:mod:`verticapy.machine_learning.memmodel.linear_model`.
+
+    .. ipython:: python
+            :suppress:
+
+        model_lm = LinearModel(coefficients, intercept)
+
+    Create a dataset.
+
+    .. ipython:: python
+            :suppress:
+
+        data = [[1.0, 0.3], [2.0, -0.6]]
+
+    **Making In-Memory Predictions**
+
+    Use :py:mod:`verticapy.machine_learning.memmodel.linear_model.LinearModel.predict` method to do predictions
+
+    .. ipython:: python
+            :suppress:
+
+        model_lm.predict(data)
+
+    **Deploy SQL Code**
+
+    Let's use the following column names:
+
+    .. ipython:: python
+            :suppress:
+
+        cnames = ['col1', 'col2']
+
+    Use :py:mod:`verticapy.machine_learning.memmodel.linear_model.LinearModel.predict_sql` method to get the SQL code needed to deploy the model using its attributes
+
+    .. ipython:: python
+            :suppress:
+
+        model_lm.predict_sql(cnames)
+
+    .. hint:: This object can be pickled and used in any in-memory environment, just like `SKLEARN <https://scikit-learn.org/>`_ models.
+
     """
 
     # Properties.
@@ -160,7 +225,7 @@ def predict_proba_sql(self, X: ArrayLike) -> list[str]:
 
 class LinearModelClassifier(LinearModel):
     """
-    InMemoryModel Implementation of linear algorithms for
+    :py:mod:`verticapy.machine_learning.memmodel.base.InMemoryModel` Implementation of linear algorithms for
     classification.
 
     Parameters
@@ -169,6 +234,82 @@ class LinearModelClassifier(LinearModel):
         ArrayLike of the model's coefficients.
     intercept: float, optional
         The intercept or constant value.
+
+    Examples
+    --------
+
+    **Initalization**
+
+    Import the required module.
+
+    .. ipython:: python
+            :suppress:
+
+        from verticapy.machine_learning.memmodel.linear_model import LinearModelClassifier
+
+    A linear classifier model is defined by its coefficients and an intercept value. In this example, we will use the following:
+
+    .. ipython:: python
+            :suppress:
+
+        coefficients = [0.5, 1.2]
+        intercept = 2.0
+
+    Let's create a :py:mod:`verticapy.machine_learning.memmodel.linear_model`.
+
+    .. ipython:: python
+            :suppress:
+
+        model_lmc = LinearModelClassifier(coefficients, intercept)
+
+    Create a dataset.
+
+    .. ipython:: python
+            :suppress:
+
+        data = [[1.0, 0.3], [-0.5, -0.8]]
+
+    **Making In-Memory Predictions**
+
+    Use :py:meth:`verticapy.machine_learning.memmodel.linear_model.LinearModelClassifier.predict` method to do predictions
+
+    .. ipython:: python
+            :suppress:
+
+        model_lmc.predict(data)
+
+    Use :py:meth:`verticapy.machine_learning.memmodel.linear_model.LinearModel.predict_proba` method to calculate the predicted probabilities for each class
+
+    .. ipython:: python
+            :suppress:
+
+        model_lmc.predict_proba(data)
+
+    **Deploy SQL Code**
+
+    Let's use the following column names:
+
+    .. ipython:: python
+            :suppress:
+
+        cnames = ['col1', 'col2']
+
+    Use :py:meth:`verticapy.machine_learning.memmodel.linear_model.LinearModelClassifier.predict_sql` method to get the SQL code needed to deploy the model using its attributes
+
+    .. ipython:: python
+            :suppress:
+
+        model_lmc.predict_sql(cnames)
+
+    Use :py:meth:`verticapy.machine_learning.memmodel.linear_model.LinearModel.predict_proba_sql` method to get the SQL code needed to deploy the model that computes predicted probabilities
+
+    .. ipython:: python
+            :suppress:
+
+        model_lmc.predict_proba_sql(cnames)
+
+    .. hint:: This object can be pickled and used in any in-memory environment, just like `SKLEARN <https://scikit-learn.org/>`_ models.
+
     """
 
     # Properties.