Function xgboost

machine_learning/xgboost_regressor.py:20–37

>>> 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+

(
    features: np.ndarray, target: np.ndarray, test_features: np.ndarray
)

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


def main() -> None: