machine_learning.xgboost_regressor
==================================

.. py:module:: machine_learning.xgboost_regressor


Functions
---------

.. autoapisummary::

   machine_learning.xgboost_regressor.data_handling
   machine_learning.xgboost_regressor.main
   machine_learning.xgboost_regressor.xgboost


Module Contents
---------------

.. py:function:: data_handling(data: dict) -> tuple

   >>> data_handling((
   ...  {'data':'[ 8.3252 41. 6.9841269 1.02380952  322. 2.55555556   37.88 -122.23 ]'
   ...  ,'target':([4.526])}))
   ('[ 8.3252 41. 6.9841269 1.02380952  322. 2.55555556   37.88 -122.23 ]', [4.526])


.. py:function:: main() -> None

   The URL for this algorithm
   https://xgboost.readthedocs.io/en/stable/
   California house price dataset is used to demonstrate the algorithm.

   Expected error values:
   Mean Absolute Error: 0.30957163379906033
   Mean Square Error: 0.22611560196662744


.. py:function:: xgboost(features: numpy.ndarray, target: numpy.ndarray, test_features: numpy.ndarray) -> numpy.ndarray

   >>> xgboost(np.array([[ 2.3571 ,   52. , 6.00813008, 1.06775068,
   ...    907. , 2.45799458,   40.58 , -124.26]]),np.array([1.114]),
   ... np.array([[1.97840000e+00,  3.70000000e+01,  4.98858447e+00,  1.03881279e+00,
   ...    1.14300000e+03,  2.60958904e+00,  3.67800000e+01, -1.19780000e+02]]))
   array([[1.1139996]], dtype=float32)