Use ridge regression when a linear predictive model benefits from L2 coefficient shrinkage, especially with correlated features or unstable ordinary least squares estimates.

Feature values

Required list input.

Target values

Required list input.

Lambda

Required number input.

Fit a one-feature ridge regression with an unpenalized intercept. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

Ridge model

The resulting ridge model returned as an object.

• Results depend on feature scaling, regularization strength, intercept handling, leakage, collinearity, outliers, residual assumptions, and data distribution. Shrinkage introduces bias and does not perform exact feature selection.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Tune regularization with nested validation, standardize using training data only, inspect residuals and coefficient stability, and compare ordinary least squares, lasso, and nonlinear baselines.

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