AI engineering glossary · Classical ML algorithms
What is ridge regression?
Ridge regression (also called L2-regularised regression, Tikhonov regularisation): A variant of least-squares fitting that adds a penalty equal to the sum of squared weights, scaled by a strength lambda. The penalty shrinks weights towards zero without making them exactly zero, which steadies the fit when features overlap or when there are many features compared with rows.
Ridge regression: an example
In scikit-learn, Ridge(alpha=1.0).fit(X, y) trains a model whose weights are smaller than plain least squares would give on correlated columns.
Related terms
More Classical ML algorithms terms
- Scree plot
- Shrinkage
- Sigmoid function
- Silhouette score
- Simple linear regression
- Soft margin
- Softmax regression
- Spectral clustering
Where you learn it
Ridge regression is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.