Created LGBM classifier @ lgbm_classifier.py #11759
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Adding LGBM Classifier Script for this repository.
for more information, see https://pre-commit.ci
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| return classifier | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_classifier.py, please provide doctest for the function main
| return predictions | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_regressor.py, please provide doctest for the function main
for more information, see https://pre-commit.ci
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| return classifier | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_classifier.py, please provide doctest for the function main
| return predictions | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_regressor.py, please provide doctest for the function main
There was a problem hiding this comment.
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| return classifier | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_classifier.py, please provide doctest for the function main
| return predictions | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_regressor.py, please provide doctest for the function main
for more information, see https://pre-commit.ci
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| return classifier | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_classifier.py, please provide doctest for the function main
| return predictions | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_regressor.py, please provide doctest for the function main
There was a problem hiding this comment.
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Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.
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@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| return classifier | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_classifier.py, please provide doctest for the function main
| return predictions | ||
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||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_regressor.py, please provide doctest for the function main
There was a problem hiding this comment.
Click here to look at the relevant links ⬇️
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Repository:
Python:
Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.
algorithms-keeper commands and options
algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| return classifier | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_classifier.py, please provide doctest for the function main
| return predictions | ||
|
|
||
|
|
||
| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_regressor.py, please provide doctest for the function main
for more information, see https://pre-commit.ci
There was a problem hiding this comment.
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Repository:
Python:
Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.
algorithms-keeper commands and options
algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| return classifier | ||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_classifier.py, please provide doctest for the function main
| return predictions | ||
|
|
||
|
|
||
| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_regressor.py, please provide doctest for the function main
There was a problem hiding this comment.
Click here to look at the relevant links ⬇️
🔗 Relevant Links
Repository:
Python:
Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.
algorithms-keeper commands and options
algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| return classifier | ||
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||
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| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_classifier.py, please provide doctest for the function main
| return predictions | ||
|
|
||
|
|
||
| def main() -> None: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file machine_learning/lgbm_regressor.py, please provide doctest for the function main
for more information, see https://pre-commit.ci
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What does LGBM stand for and why is the reader forced to guess? |
LGBM stands for Light Gradient Boosting Machine. It's an implementation of gradient boosting that is designed to be highly efficient, fast, and scalable. The name "Light" comes from the fact that it consumes less memory and is faster than traditional gradient boosting implementations like XGBoost. LGBM achieves this by using techniques like histogram-based learning and leaf-wise growth strategies. No guessing sir, I should have written like this. |
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Closing tests_are_failing PRs to prepare for Hacktoberfest |
Adding LGBM Classifier Script for this repository.
Describe your change:
I am adding an implementation of the LGBM Classifier & LGBM Regressor using the Iris dataset as an example. This script demonstrates how to use LightGBM to train a model and visualize its performance through a confusion matrix. It includes proper type hints, docstrings, and doctests to ensure clarity and functionality.
Checklist: