xgboost regressor example split dataset into features and target data is features datahandling 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 xgboostnp array 2 3571 52 6 00813008 1 06775068 907 2 45799458 40 58 124 26 np array1 114 np array1 97840000e00 3 70000000e01 4 98858447e00 1 03881279e00 1 14300000e03 2 60958904e00 3 67800000e01 1 19780000e02 array1 1139996 dtypefloat32 predict target for test data the url for this algorithm https xgboost readthedocs ioenstable california house price dataset is used to demonstrate the algorithm expected error values mean absolute error 0 30957163379906033 mean square error 0 22611560196662744 load california house price dataset error printing xgboost regressor example split dataset into features and target data is features 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 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 predict target for test data 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 load california house price dataset error printing
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def data_handling(data: dict) -> tuple:
    
    return (data["data"], data["target"])


def xgboost(
    features: np.ndarray, target: np.ndarray, test_features: np.ndarray
) -> np.ndarray:
    
    xgb = XGBRegressor(
        verbosity=0, random_state=42, tree_method="exact", base_score=0.5
    )
    xgb.fit(features, target)
    
    predictions = xgb.predict(test_features)
    predictions = predictions.reshape(len(predictions), 1)
    return predictions


def main() -> None:
    
    
    california = fetch_california_housing()
    data, target = data_handling(california)
    x_train, x_test, y_train, y_test = train_test_split(
        data, target, test_size=0.25, random_state=1
    )
    predictions = xgboost(x_train, y_train, x_test)
    
    print(f"Mean Absolute Error: {mean_absolute_error(y_test, predictions)}")
    print(f"Mean Square Error: {mean_squared_error(y_test, predictions)}")


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()