From 71b372f5e2fd313268018df237d401efd7795464 Mon Sep 17 00:00:00 2001 From: Tianyi Zheng Date: Sat, 14 Oct 2023 09:34:05 -0400 Subject: [PATCH] Remove doctest in `xgboost_regressor.py` main function (#10422) * updating DIRECTORY.md * updating DIRECTORY.md * updating DIRECTORY.md * updating DIRECTORY.md * Update xgboost_regressor.py --------- Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com> --- machine_learning/xgboost_regressor.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/machine_learning/xgboost_regressor.py b/machine_learning/xgboost_regressor.py index a540e3ab0..52e041c55 100644 --- a/machine_learning/xgboost_regressor.py +++ b/machine_learning/xgboost_regressor.py @@ -39,13 +39,13 @@ def xgboost( def main() -> None: """ - >>> main() - Mean Absolute Error : 0.30957163379906033 - Mean Square Error : 0.22611560196662744 - 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 california = fetch_california_housing() @@ -55,8 +55,8 @@ def main() -> None: ) predictions = xgboost(x_train, y_train, x_test) # Error printing - print(f"Mean Absolute Error : {mean_absolute_error(y_test, predictions)}") - print(f"Mean Square Error : {mean_squared_error(y_test, predictions)}") + 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__":