Glossary term
Glossary term
Foundations
A type of regularization that penalizes weights in proportion to the sum of the absolute value of the weights. L1 regularization helps drive the weights of irrelevant or barely relevant features to exactly 0. A feature with a weight of 0 is effectively removed from the model.
Contrast with L2 regularization.
Created for this library
A risk modeling team uses L1 regularization to encourage sparse coefficients and produce an interpretable scorecard.
A marketing analytics team uses L1 regularization to select features automatically from a large pool of candidate signals.
A research team uses L1 regularization to build a small, sparse model that fits on an embedded device.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License