Fixes: #12108: Add Ridge regression implementation to machine_learning #12251
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algorithms-keeper
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require descriptive names
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| self.num_iterations: int = num_iterations | ||
| self.theta: np.ndarray = None | ||
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| def feature_scaling( |
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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/ridge_regression/model.py, please provide doctest for the function feature_scaling
| self.theta: np.ndarray = None | ||
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| def feature_scaling( | ||
| self, x: np.ndarray |
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Please provide descriptive name for the parameter: x
| x_scaled = (x - mean) / std | ||
| return x_scaled, mean, std | ||
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| def fit(self, x: np.ndarray, y: np.ndarray) -> None: |
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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/ridge_regression/model.py, please provide doctest for the function fit
Please provide descriptive name for the parameter: x
Please provide descriptive name for the parameter: y
| ) / m | ||
| self.theta -= self.alpha * gradient # updating weights | ||
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| def predict(self, x: np.ndarray) -> np.ndarray: |
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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/ridge_regression/model.py, please provide doctest for the function predict
Please provide descriptive name for the parameter: x
| ) * np.sum(self.theta**2) | ||
| return cost | ||
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| def mean_absolute_error(self, 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/ridge_regression/model.py, please provide doctest for the function mean_absolute_error
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ankana2113
changed the title
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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.
| self.theta: np.ndarray = None | ||
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||
| def feature_scaling( | ||
| self, x: np.ndarray |
There was a problem hiding this comment.
Please provide descriptive name for the parameter: x
| x_scaled = (x - mean) / std | ||
| return x_scaled, mean, std | ||
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| def fit(self, x: np.ndarray, y: np.ndarray) -> None: |
There was a problem hiding this comment.
Please provide descriptive name for the parameter: x
Please provide descriptive name for the parameter: y
| ) / m | ||
| self.theta -= self.alpha * gradient # updating weights | ||
|
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| def predict(self, x: np.ndarray) -> np.ndarray: |
There was a problem hiding this comment.
Please provide descriptive name for the parameter: x
| x_scaled, _, _ = self.feature_scaling(x) | ||
| return x_scaled.dot(self.theta) | ||
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| def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float: |
There was a problem hiding this comment.
Please provide descriptive name for the parameter: x
Please provide descriptive name for the parameter: y
Describe your change:
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