Lasso Regression

Lasso Regression is what you reach for when a plain linear regression starts to feel too “eager”—using every feature a little bit, even when many of them are noise. It keeps the familiar linear model shape, but adds a built-in preference for simplicity.

What it is and how it works

Lasso is a linear regression model trained with an extra penalty on the size of the coefficients. Concretely, it minimizes the usual squared prediction errors plus an L1 regularization term: the sum of the absolute values of the coefficients. That L1 penalty has a special effect: it can drive some coefficients to exactly zero. When a coefficient becomes zero, that feature is effectively removed from the model, so Lasso performs feature selection while fitting.

Why the L1 penalty changes behavior

Think of the penalty as a “budget” for coefficient magnitude. Lasso spends that budget sparingly, preferring a few strong signals over many tiny ones. The strength of this push is controlled by a hyperparameter (commonly called alpha or λ): larger values mean more shrinkage and more zeros. Because the penalty depends on coefficient scale, feature scaling (standardizing inputs) is usually important so the penalty treats features fairly.

Where you see it in practice

Credit risk: selecting a small set of stable predictors from dozens of correlated financial variables.
House price prediction: filtering out weak neighborhood indicators while keeping the strongest drivers.
Demand forecasting: choosing a few meaningful lag/seasonality features from many candidates.

In scikit-learn you’ll encounter it as

from sklearn.linear_model import Lasso

and you typically tune alpha with cross-validation. Ignoring regularization in high-dimensional data can lead to unstable coefficients and poor generalization; Lasso directly addresses that by trading a bit of bias for lower variance and a simpler model.

Lasso Regression is a linear regression method that fits coefficients by minimizing squared error with an added L1 regularization penalty (the sum of absolute coefficient values). The L1 penalty drives some coefficients exactly to zero, performing built-in feature selection and yielding sparse, interpretable models. It matters because it reduces overfitting and stabilizes prediction in high-dimensional settings, especially when many features are irrelevant or redundant.

Imagine packing for a trip with a small suitcase. You bring the items that matter most, and you leave the “maybe” stuff behind. Lasso Regression is like that for prediction: it tries to predict a number (like a house price) while also keeping the model simple by dropping unhelpful inputs.

In supervised learning, you train on examples where you already know the right answer. With Lasso Regression, the model is encouraged to give some factors exactly zero importance, effectively removing them. This can make predictions more stable, easier to explain, and less likely to chase noise—especially when you have lots of possible signals, like many patient measurements or many website activity features.