Add the documentation for Sklearn integration in EVADB.

docs/source/reference/ai/model-train-sklearn.rst

@@ -6,21 +6,54 @@ Model Training with Sklearn
 1. Installation
 ---------------
 
-To use the `Sklearn framework <https://scikit-learn.org/stable/>`_, we need to install the extra sklearn dependency in your EvaDB virtual environment.
+To use the `Flaml XGBoost AutoML framework <https://microsoft.github.io/FLAML/docs/Examples/Integrate%20-%20Scikit-learn%20Pipeline/>`_, we need to install the extra Flaml dependency in your EvaDB virtual environment.
 
 .. code-block:: bash
-   
-   pip install evadb[sklearn]
+
+   pip install "flaml[automl]"
 
 2. Example Query
 ----------------
 
 .. code-block:: sql
 
-   CREATE OR REPLACE FUNCTION PredictHouseRent FROM
+   CREATE FUNCTION IF NOT EXISTS PredictRent FROM
    ( SELECT number_of_rooms, number_of_bathrooms, days_on_market, rental_price FROM HomeRentals )
-   TYPE Sklearn
+   TYPE XGBoost
    PREDICT 'rental_price';
 
-In the above query, you are creating a new customized function by training a model from the ``HomeRentals`` table using the ``Sklearn`` framework.
-The ``rental_price`` column will be the target column for predication, while the rest columns from the ``SELECT`` query are the inputs. 
+In the above query, you are creating a new customized function by training a model from the ``HomeRentals`` table using the ``Flaml XGBoost`` framework.
+The ``rental_price`` column will be the target column for predication, while the rest columns from the ``SELECT`` query are the inputs.
+
+3. Model Training Parameters
+----------------------------
+
+.. list-table:: Available Parameters
+   :widths: 25 75
+
+   * - PREDICT (**required**)
+     - The name of the column we wish to predict.
+   * - MODEL
+     - The Sklearn models supported as of now are ``Random Forest``, ``Extra Trees Regressor`` and ``KNN``.
+       You can use ``rf`` for Random Forests, ``extra_tree`` for ExtraTrees Regressor, and ``kneighbor`` for KNN.
+   * - TIME_LIMIT
+     - Time limit to train the model in seconds. Default: 120.
+   * - TASK
+     - Specify whether you want to perform ``regression`` task or ``classification`` task.
+   * - METRIC
+     - Specify the metric that you want to use to train your model. For e.g. for training ``regression`` tasks you could
+       use the ``r2`` or ``RMSE`` metrics. For training ``classification`` tasks you could use the ``accuracy`` or ``f1_score`` metrics.
+       More information about the model metrics could be found `here <https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#optimization-metric>`_
+
+Below are the example queries specifying the aboe parameters
+
+.. code-block:: sql
+
+   CREATE OR REPLACE FUNCTION PredictHouseRentSklearn FROM
+   ( SELECT number_of_rooms, number_of_bathrooms, days_on_market, rental_price FROM HomeRentals )
+   TYPE Sklearn
+   PREDICT 'rental_price'
+   MODEL 'extra_tree'
+   METRIC 'r2'
+   TASK 'regression'
+   TIME_LIMIT 180;