ridge
Builtin index · Statistics & ML
result = ridge(X, y, alpha)
Input: X, y, alpha · Output: result
What it is
Tikhonov-regularized (L2) least squares on an N-row, D-column feature matrix X and a length-N target vector y, with regularization strength alpha (a non-negative number; plain OLS at alpha = 0).
Overloads
One call shape — this function is not overloaded.
| Code | Input | Output | Description |
result = ridge(X, y, alpha) | X matrix, vector, or numberTikhonov-regularized (L2) least squares on an N-row, D-column feature matrix X and a length-N target vector y, with regularization strength alpha (a non-negative number.y matrix, vector, or numberTikhonov-regularized (L2) least squares on an N-row, D-column feature matrix X and a length-N target vector y, with regularization strength alpha (a non-negative number.alpha —plain OLS at alpha = 0). | result vector | Returns only the length-D coefficient vector, with no intercept term. |
Examples
From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.
X = randn(120, 3, seed = 3)
beta = [2.0, -1.0, 0.5]
y = X * beta + 0.1 * randn(120, seed = 4)
c = ridge(X, y, 0.5)
print("ridge coef: {round(c, 3)}") # close to [2.0, -1.0, 0.5]
output
ridge coef: [1.993, -1.002, 0.496]
from statistics-ml