Add Ridge Regression to Machine Learning by Harmanaya · Pull Request #12246 · TheAlgorithms/Python

Harmanaya · 2024-10-22T17:56:00Z

Describe your change:

Add an algorithm?
Fix a bug or typo in an existing algorithm?
Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
Documentation change?

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algorithms-keeper · 2024-10-22T17:56:06Z

+
+
+class RidgeRegression:
+    def __init__(self, alpha=0.001, lambda_=0.1, iterations=1000):


Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: alpha

Please provide type hint for the parameter: lambda_

Please provide type hint for the parameter: iterations

algorithms-keeper · 2024-10-22T17:56:06Z

+        self.iterations = iterations
+        self.theta = None
+
+    def feature_scaling(self, X):


Please provide return type hint for the function: feature_scaling. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression.py, please provide doctest for the function feature_scaling

Please provide type hint for the parameter: X

Please provide descriptive name for the parameter: X

algorithms-keeper · 2024-10-22T17:56:06Z

+        # Avoid division by zero for constant features (std = 0)
+        std[std == 0] = 1  # Set std=1 for constant features to avoid NaN
+
+        X_scaled = (X - mean) / std


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled

algorithms-keeper · 2024-10-22T17:56:06Z

+        X_scaled = (X - mean) / std
+        return X_scaled, mean, std
+
+    def fit(self, X, y):


Please provide return type hint for the function: fit. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression.py, please provide doctest for the function fit

Please provide type hint for the parameter: X

Please provide descriptive name for the parameter: X

Please provide type hint for the parameter: y

Please provide descriptive name for the parameter: y

algorithms-keeper · 2024-10-22T17:56:07Z

+        :param X: Input features, shape (m, n)
+        :param y: Target values, shape (m,)
+        """
+        X_scaled, mean, std = self.feature_scaling(X)  # Normalize features


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled

algorithms-keeper · 2024-10-22T17:56:07Z

+            gradient = (X_scaled.T.dot(error) + self.lambda_ * self.theta) / m
+            self.theta -= self.alpha * gradient  # Update weights
+
+    def predict(self, X):


Please provide return type hint for the function: predict. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression.py, please provide doctest for the function predict

Please provide type hint for the parameter: X

Please provide descriptive name for the parameter: X

algorithms-keeper · 2024-10-22T17:56:07Z

+        :param X: Input features, shape (m, n)
+        :return: Predicted values, shape (m,)
+        """
+        X_scaled, _, _ = self.feature_scaling(X)  # Scale features using training data


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled

algorithms-keeper · 2024-10-22T17:56:07Z

+        X_scaled, _, _ = self.feature_scaling(X)  # Scale features using training data
+        return X_scaled.dot(self.theta)
+
+    def compute_cost(self, X, y):


Please provide return type hint for the function: compute_cost. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression.py, please provide doctest for the function compute_cost

Please provide type hint for the parameter: X

Please provide descriptive name for the parameter: X

Please provide type hint for the parameter: y

Please provide descriptive name for the parameter: y

algorithms-keeper · 2024-10-22T17:56:07Z

+        :param y: Target values, shape (m,)
+        :return: Computed cost
+        """
+        X_scaled, _, _ = self.feature_scaling(X)  # Scale features using training data


Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_scaled

algorithms-keeper · 2024-10-22T17:56:07Z

+        ) * np.sum(self.theta**2)
+        return cost
+
+    def mean_absolute_error(self, y_true, y_pred):


Please provide return type hint for the function: mean_absolute_error. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression.py, please provide doctest for the function mean_absolute_error

Please provide type hint for the parameter: y_true

Please provide type hint for the parameter: y_pred

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Repository:

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Type hints

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unittest

pytest

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algorithms-keeper · 2024-10-22T18:29:02Z

+        scaled_features = (features - mean) / std
+        return scaled_features, mean, std
+
+    def fit(self, x: np.ndarray, y: np.ndarray) -> None:


As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression.py, please provide doctest for the function fit

Please provide descriptive name for the parameter: x

Please provide descriptive name for the parameter: y

algorithms-keeper · 2024-10-22T18:29:02Z

+            gradient = (x_scaled.T.dot(error) + self.lambda_ * self.theta) / m
+            self.theta -= self.alpha * gradient  # Update weights
+
+    def predict(self, x: np.ndarray) -> np.ndarray:


As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression.py, please provide doctest for the function predict

Please provide descriptive name for the parameter: x

algorithms-keeper · 2024-10-22T18:29:02Z

+        x_scaled, _, _ = self.feature_scaling(x)  # Scale features using training data
+        return x_scaled.dot(self.theta)
+
+    def compute_cost(self, x: np.ndarray, y: np.ndarray) -> float:


As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression.py, please provide doctest for the function compute_cost

Please provide descriptive name for the parameter: x

Please provide descriptive name for the parameter: y

algorithms-keeper · 2024-10-22T18:29:02Z

+        ) * np.sum(self.theta**2)
+        return cost
+
+    def mean_absolute_error(self, y_true: np.ndarray, y_pred: np.ndarray) -> float:


As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression.py, please provide doctest for the function mean_absolute_error

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Harmanaya · 2024-10-22T19:23:38Z

@cclauss Could you please review this PR when you get a chance?

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cclauss · 2026-09-15T16:22:13Z

@priya-sundaram-dev, please review this pull request vs.

machine_learning: add RidgeRegression with tests and demo #14016

priya-sundaram-dev · 2026-09-15T16:33:18Z

Reviewed this against #14016 as requested. Both implement a gradient-descent Ridge with the same public shape (fit/predict/compute_cost). Quick comparison and one blocking correctness issue on this PR:

Correctness (this PR, #12246): predict and compute_cost call feature_scaling(features) again, re-computing mean/std from whatever data is passed in. That means predictions on new/unseen data are scaled by that data's own statistics, not the training set's — so theta (fit on the training scaling) is applied to differently-scaled inputs and the results are inconsistent. The fix is to persist the scaling from fit:

def fit(self, features, target):
    features_scaled, self._mean, self._std = self.feature_scaling(features)
    ...

def predict(self, features):
    if self.theta is None:
        raise ValueError("Model is not trained yet. Call `fit` first.")
    features_scaled = (features - self._mean) / self._std
    return features_scaled.dot(self.theta)

Minor: mean_absolute_error is annotated -> float but returns a NumPy scalar (wrap in float(...)); the __main__ demo prepends an intercept column that then gets std-normalized to 0 and L2-penalized, so there's no true unregularized bias term — worth a comment or explicit handling.

vs #14016: that PR ships cleaner separate pytest tests, but it also adds machine_learning/__init__.py and a machine_learning/tests/ package with a conftest.py. This repo is a collection of standalone, doctested scripts rather than an installed package, so introducing package __init__.py + a tests dir diverges from the established convention (doctest/pytest --doctest-modules in-file).

Recommendation: the single-file, in-file-doctest approach here (#12246) fits repo conventions better — but it needs the training-scale fix above before merge. If we'd rather take #14016 for its tests, those tests should move to in-file doctests and drop the new __init__.py/tests/ scaffolding. Either way we should close one to avoid the duplicate. Happy to re-review once the scaling fix lands.

cclauss · 2026-09-15T17:32:42Z

I will put awaiting changes and bug labels on both issues and we will see who fixes what. Tiime is limited because we will close all awaiting changes pull requests before the October 1st start of Hacktoberfest.

Fix issue TheAlgorithms#12108: Added Ridge Regression to Machine Lear…

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algorithms-keeper Bot added require descriptive names This PR needs descriptive function and/or variable names require tests Tests [doctest/unittest/pytest] are required require type hints https://docs.python.org/3/library/typing.html labels Oct 22, 2024

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Open Pull Request File Map #15310

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Merge branch 'master' into issue-12108

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Add Ridge Regression to Machine Learning #12111

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algorithms-keeper Bot added the merge conflicts Open a new PR or rebase on the latest commit label Sep 15, 2026

Merge branch 'master' into issue-12108

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cclauss changed the title ~~Fixes issue #12108: Added Ridge Regression to Machine Learning~~ Add Ridge Regression to Machine Learning Sep 15, 2026

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		class RidgeRegression:
		def __init__(self, alpha=0.001, lambda_=0.1, iterations=1000):

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