Write a Python function ridge_loss that implements the Ridge Regression loss function. The function should take a 2D numpy array X representing the feature matrix, a 1D numpy array w representing the coefficients, a 1D numpy array y_true representing the true labels, and a float alpha representing the regularization parameter. The function should return the Ridge loss, which combines the Mean Squared Error (MSE) and a regularization term.
Examples
Example 1:
Input:
import numpy as np
X = np.array([[1, 1], [2, 1], [3, 1], [4, 1]])
w = np.array([0.2, 2])
y_true = np.array([2, 3, 4, 5])
alpha = 0.1
loss = ridge_loss(X, w, y_true, alpha)
print(loss)Output:
2.204Explanation: The Ridge loss is calculated using the Mean Squared Error (MSE) and a regularization term. The output represents the combined loss value.
Starter Code
import numpy as np
def ridge_loss(X: np.ndarray, w: np.ndarray, y_true: np.ndarray, alpha: float) -> float:
# Your code here
pass
Python3
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