added mean absolute percentage error #10464
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algorithms-keeper
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added
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require tests
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| def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> float: |
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As there is no test file in this pull request nor any test function or class in the file machine_learning/loss_functions/mean_absolute_percentage_error.py, please provide doctest for the function mean_absolute_percentage_error
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sufyanahmed7
approved these changes
Oct 16, 2023
tianyizheng02
requested changes
Oct 22, 2023
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
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tianyizheng02
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Oct 24, 2023
Done as requested. |
tianyizheng02
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Oct 24, 2023
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tianyizheng02
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Oct 25, 2023
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Sep 15, 2024
* added mean absolute percentage error * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * added mean_absolute_percentage_error * added mean_absolute_percentage_error * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * added mean_absolute_percentage_error * added mean_absolute_percentage_error * added mean absolute percentage error * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * added mean absolute percentage error * added mean absolute percentage error * added mean absolute percentage error * added mean absolute percentage error * added mean absolute percentage error * Update machine_learning/loss_functions.py --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Tianyi Zheng <[email protected]>
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Describe your change:
I added mean absolute percentage error to the loss function directory. I used matrix operations instead of for loops to enhance the performance of the algorithm. Looking forward to getting this pull request merged and also to acquire the hacktoberfest and hacktoberfest-accepted badge..
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