Update lgbm_regressor.py

Author: ArchismanKarmakar

machine_learning/lgbm_regressor.py

@@ -17,15 +17,14 @@ def data_handling(data: dict) -> tuple:
     return (data["data"], data["target"])
 
 
-def lgbm_regressor(
-    features: np.ndarray, target: np.ndarray, test_features: np.ndarray
-) -> np.ndarray:
+def lgbm_regressor(features: np.ndarray, target: np.ndarray, 
+                   test_features: np.ndarray) -> np.ndarray:
     """
-    >>> lgbm_regressor(np.array([[ 0.12,  0.02,  0.01,  0.25,  0.09]]), np.array([1]),
-    ... np.array([[0.11,  0.03,  0.02,  0.28,  0.08]]))
+    >>> lgbm_regressor(np.array([[0.12, 0.02, 0.01, 0.25, 0.09]]), 
+    ... np.array([1]), np.array([[0.11, 0.03, 0.02, 0.28, 0.08]]))
     array([[0.98]], dtype=float32)
     """
-    lgbm = LGBMRegressor(verbosity=0, random_state=42)
+    lgbm = LGBMRegressor(random_state=42)
     lgbm.fit(features, target)
     # Predict target for test data
     predictions = lgbm.predict(test_features)
@@ -38,13 +37,9 @@ def main() -> None:
     The URL for this algorithm:
     https://lightgbm.readthedocs.io/en/latest/
     Bank Marketing Dataset is used to demonstrate the algorithm.
-
-    Expected error values:
-    Mean Absolute Error: 0.2 (approx.)
-    Mean Square Error: 0.15 (approx.)
     """
     # Load Bank Marketing dataset
-    bank_data = fetch_openml(name="bank-marketing", version=1, as_frame=False)
+    bank_data = fetch_openml(name='bank-marketing', version=1, as_frame=False)
     data, target = data_handling(bank_data)
     x_train, x_test, y_train, y_test = train_test_split(
         data, target, test_size=0.25, random_state=1