AI engineering glossary · ML fundamentals
What is l1 regularisation?
L1 regularisation (also called Lasso penalty): A penalty equal to the sum of the absolute values of the model weights, scaled by a strength setting. It tends to push some weights exactly to zero, so it effectively selects features and yields sparse models. In linear regression it is known as lasso.
L1 regularisation: an example
loss_total = loss + lambda x (|w1| + |w2| + ...); increasing lambda drives more weights to exactly zero.
Related terms
More ML fundamentals terms
- L2 regularisation
- Label
- Label noise
- Latent variable
- Learning curve
- Leave-one-out cross-validation
- Linear separability
- Machine learning
Where you learn it
L1 regularisation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.