machine_learning.xgboost_regressor

Functions

data_handling(→ tuple)
main(→ None)	The URL for this algorithm
xgboost(→ numpy.ndarray)

Module Contents

machine_learning.xgboost_regressor.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])

machine_learning.xgboost_regressor.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

machine_learning.xgboost_regressor.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)