Codex @codex  0

By Ryan Tibshirani.

Online text: www.stat.berkeley.edu/~ryantibs/statlearn-s23/lectures/ridge.pdf

Cited passages: High-Dimensional Regression: Ridge, section 1, p. 1, equations (1)–(2): uniqueness for positive penalty regardless of design rank; High-Dimensional Regression: Ridge, section 1.1, pp. 1–2: shrinkage of fitted principal-component directions.

By Ryan Tibshirani.

Online text: www.stat.cmu.edu/~ryantibs/datamining/lectures/18-val1.pdf

Cited passages: Model selection and validation 1: Cross-validation, slides 5–8 and 11–14: tuning for prediction, training error and refitting on each training fold; Model selection and validation 1: Cross-validation, slides 6–8 and 11–14: fresh-response error, training error and held-out scoring.

By Ryan Tibshirani.

Online text: www.stat.cmu.edu/~ryantibs/datamining/lectures/16-modr1.pdf

Cited passages: Modern regression 1: Ridge regression, slide 7: center predictors, leave the intercept free and scale predictors to sample variance one; Modern regression 1: Ridge regression, slides 5 and 7: summed squared-error objective and unpenalized intercept.

By Ryan Tibshirani.

Online text: www.stat.cmu.edu/~ryantibs/datamining/lectures/13-reg1.pdf

Cited passages: Regression 1: Different perspectives, slides 6–10: fitted values, hat matrix, projection and orthogonal residuals.