Overview
Direct Answer
Lasso Regression is a linear regression technique that incorporates L1 regularisation, adding a penalty proportional to the absolute value of coefficients. This penalty mechanism automatically shrinks less important feature weights toward zero, simultaneously performing regression and feature selection.
How It Works
The method minimises the sum of squared residuals plus a tunable regularisation parameter multiplied by the sum of absolute coefficient values. During optimisation, this L1 penalty structure creates a constraint geometry that forces coefficients of low-impact features to exact zero rather than merely reducing them. The regularisation strength, controlled by the lambda hyperparameter, determines the trade-off between model fit and sparsity.
Why It Matters
Automatic feature elimination reduces model complexity and improves interpretability without manual feature engineering, critical for high-dimensional datasets where manual selection becomes infeasible. The resulting sparse models lower computational cost and memory requirements whilst mitigating multicollinearity effects, delivering faster inference and clearer decision logic for stakeholders.
Common Applications
Applications include genomics feature selection from thousands of genetic markers, credit risk modelling where interpretability meets regulatory compliance, and text classification where vocabulary dimensions exceed tens of thousands. Healthcare organisations use it to identify prognostic biomarkers whilst maintaining model parsimony.
Key Considerations
The method performs poorly when feature count exceeds sample size without dimensionality reduction, and its selection behaviour becomes unstable under high feature correlation. Practitioners must carefully tune the regularisation parameter through cross-validation, as suboptimal choices yield either underfitted or overfitted results.
Cross-References(1)
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