Ridge regression

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover ridge regression?

Explains squared penalties, proportional shrinkage, coefficient behavior, and a preprocessing-to-evaluation pipeline.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

Ridge is a regularized linear regression estimator that penalizes the squared magnitude of coefficients.

Ridge regression is an L2-regularized linear model that penalizes squared coefficient magnitudes. It shrinks coefficients toward zero, giving larger coefficients stronger penalties without usually removing features entirely. section 4.3.2

Ridge is especially useful when predictors overlap:

Keeps multicollinear, predictive features in the model with generally similar-sized coefficients. section 4.3.2
Attenuates coefficients proportionally rather than selecting features as L1 regularization does. section 4.3.2

A regularized polynomial pipeline

A Scikit-learn workflow expands degree-two features, standardizes them, and fits Ridge(alpha=2500.0). Five-fold cross-validation reports an RMSE of 33.738 with a 0.275 standard deviation, alongside fit and prediction timings.

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

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Also explained in

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Ensemble Methods for Machine LearningRidge regression is linear regression fitted with an L2 penalty on the coefficients to reduce model complexity.

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Related concepts

weight decayuses · Weight decay is a model-training regularization technique that discourages large parameter values by penalizing their magnitudes or shrinking them during optimization.
regularizationkind of · 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.
linear regressionequivalent to · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.
L1 regularizationcontrasts with · L1 regularization is a technique that adds a penalty proportional to the sum of absolute model-parameter values, encouraging sparse parameter estimates.
l2 normproduced by · The L2 norm is the nonnegative Euclidean magnitude of a vector, equal to the square root of the sum of its components’ squares, and to absolute value for a scalar.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.