> ML_ALGORITHM // RIDGE-LASSO-ELASTICNET_v1.0
Regularized Linear Models (Ridge, Lasso, ElasticNet)
Linear regression extended with L1 (Lasso) and L2 (Ridge) penalties to prevent overfitting and perform automated feature selection.
Linear & Generalized Modelsclassical-supervisedhigh-intrinsicsmall (<1k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(n * p^2) via coordinate descent
Inference Complexity:O(p)
Hardware Profile
CPU Friendly:Yes
Requires GPU:No
Memory Footprint:low
Interpretability & Data
Interpretability Tier:high-intrinsic
Training Data Needs:small (<1k)
Interpretability Assessment
Lasso enforces exact sparsity, setting non-informative feature weights to zero.
Suitable Tasks & Supported Modalities
Suitable Tasks:
regressionbinary classificationfeature extraction
Supported Modalities:
tabular
Implementing Libraries
Foundational Literature
Regression Shrinkage and Selection via the LassoRobert Tibshirani (1996) · Journal of the Royal Statistical Society: Series B (Methodological)
Regularization and variable selection via the elastic netHui Zou, Trevor Hastie (2005) · Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Common Pitfalls & Warnings
- Failing to standardize features before applying penalty
- Ignoring collinear feature group arbitrary selection in pure Lasso