ridge_model

Builtin index · Statistics & ML

result = ridge_model(X, y, alpha)

Input: X, y, alpha · Output: result

What it is

The same fit as ols_model on an N-row, D-column X and a length-N y, but with L2 regularization strength alpha (a non-negative number) shrinking the coefficients toward zero; ols_model is this at alpha = 0.

Overloads

One call shape — this function is not overloaded.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

r = ridge_model(X, y, 0.1)           # least squares with L2 regularization

k = kmeans_model(X, 2, seed = 42)    # .predict assigns to the nearest centroid

p = pca_model(X, 1)                  # .predict projects onto the fitted components

print("cluster of row 0: {k.predict(X)[0]}")

cluster of row 0: 1

from specialized

centre(X) := X - mean(X)
final(X, y) := ridge_model(X, y, 0.1)

pipe = pipeline("centre", "final")
fitted = pipe.fit(X, y)
print("pipeline R^2 {fitted.score(X, y):.4f}")

pipeline R^2 0.9992

from specialized

standardize_cols(M) := (M - mean(M)) ./ std(M)
final(A, b) := ridge_model(A, b, 0.1)

pipe = pipeline("standardize_cols", "final")
fitted = pipe.fit(split.X_train, split.y_train)
print("held-out R2: {round(fitted.score(split.X_test, split.y_test), 4)}")

held-out R2: 0.991

from statistics-ml

How it works

ridge_model(X, y, alpha) — ols_model plus L2 regularization on the centered design, the model-protocol sibling of the flat ridge builtin (which has no automatic intercept).

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.