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Embedded method

As explained in Machine Learning System Design

A feature-selection technique that derives feature choices from the parameters or importance values of a model trained as part of the selection process.

An embedded method selects features from the parameters or importance values of a model trained as part of the selection process. It integrates feature selection with model fitting, unlike filter methods, which rank features independently, or wrapper methods, which search subsets using model quality. section 11.3.3

Lasso regression

Lasso regression eliminates features as its L1 regularization term grows by reducing their coefficients to zero, making the model parameters drive feature selection. section 11.3.3

Figure 11.9 Lasso regression eliminates features one by one by reducing their coefficients to zero as L1 regularization term grows.
figure

Use the figure to see how increasing L1 regularization drives coefficients to zero and removes features. section 11.3.3

How Machine Learning System Design covers it

How deep it goes
Contrasts model-integrated selection with filter and wrapper families, but does not detail an algorithmic workflow.
Examples
Uses lasso regression, where increasing L1 regularization reduces coefficients to zero and eliminates features.
What you can do after
Evaluate model-integrated feature selection against independent filtering and model-wrapped subset search.
How it connects
Connects feature selection with filter methods, wrapper methods, L1 regularization, and lasso regression.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.