How deeply does Grokking Machine Learning cover regularization parameter?

Defines λ in the regularized error formula and explains zero and large values, but gives no detailed selection procedure.

Regularization parameter

As explained in Grokking Machine Learning

A training hyperparameter that controls the strength of the complexity penalty relative to predictive error.

A regularization parameter, written λ (lambda), sets how strongly training penalizes model complexity relative to regression error. The training objective adds the regularization term multiplied by λ. section 4.5

Changing λ shifts the balance between predictive performance and simplicity:

At λ = 0, the penalty disappears, leaving the unregularized regression model. section 4.5
A large λ favors a simpler model, which may fit the dataset poorly. Use validation to help choose a suitable value. section 4.5

Try it with lasso and ridge

Given a polynomial regression model, MAE error, and λ = 0.1, determine its lasso error using the L1 norm and its ridge error using the L2 norm. section 4.8

How Grokking Machine Learning covers it

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Also explained in

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Ensemble Methods for Machine LearningA regularization parameter controls the strength of the complexity penalty relative to the data-fitting loss.

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Related concepts

regularizationconfigures · Regularization is a modeling technique that constrains or penalizes solutions during fitting or optimization to discourage undesirable complexity or behavior and improve generalization or estimation.
hyperparameterkind of · A hyperparameter is a setting chosen outside ordinary parameter learning that controls a model, algorithm, or learning procedure.
cross-validationused by · Cross-validation is a resampling technique that evaluates a model by repeatedly fitting it on some portions of available data and assessing it on held-out portions.
margincontrasts with · A classification margin measures separation or confidence, expressed as distance from a decision boundary or as the correct class's score advantage over competing classes.

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