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Modified Linear Regression to work on OLS, fixes #8847 - #11311
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Good stuff! Apart from the changes that I've requested in my other comments, could you also rewrite the explanation at the top of the file to reflect your new implementation? In particular, make sure your new explanation does the following:
- Briefly explain what linear regression is
- Explain the OLS regression formula,
$(\mathbf{X}^{\top} \mathbf{X})^{-1} \mathbf{X} \mathbf{y}$ - Note possible issues with your implementation (i.e. inefficiency, numerical instability, etc.)
- Cite a source that readers can refer to for further info (Wikipedia or similar is fine)
I wrote a similar explanation for weighted regression in machine_learning/local_weighted_learning/local_weighted_learning.py a while back. If you'd like, you're welcome to use that explanation as a reference (though your explanation doesn't need to be nearly as long).
| :param data_x : contains our dataset | ||
| :param data_y : contains the output (result vector) | ||
| def run_linear_regression_ols(data_x, data_y): | ||
| """Implement Linear regression using OLS over the dataset |
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| """Implement Linear regression using OLS over the dataset | |
| """Implement OLS linear regression over a given dataset |
Slight rewording for clarity
| :return : feature for line of best fit (Feature vector) | ||
| """ |
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The OLS regression function needs doctests—make sure you verify the outputs of your tests with a calculator that can do linear regression (e.g., Wolfram Alpha)
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Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>
Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>
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Closing tests_are_failing PRs to prepare for Hacktoberfest 2024 |
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