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RECITATION 1 APRIL 14

This guide explores advanced Lasso techniques and spline methods for effective parameter selection in R. Utilizing libraries such as "lasso2" and "lars," we delve into the Lasso algorithm, its application for shrinkage factors, and its implications on model sparsity. We cover smoothing parameter selection, cross-validation methods, and the practical use of piecewise polynomials and splines to achieve local data modeling. Follow the detailed insights and code excerpts for implementation in R, ensuring robust model performance.

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RECITATION 1 APRIL 14

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  1. RECITATION 1APRIL 14 Lasso Smoothing Parameter Selection Splines

  2. Lasso – R package • l1ce() in library(“lasso2”) or lars() in library(“lars”) • l1ce( y ~ . , data = dataset, bound = shrinkage.factor) • Lasso doesn’t have EDF (why?) . We can use the shrinkage factor to get a sense of the penalty.

  3. Lasso Sparsity Intuition

  4. Lasso Solution Path

  5. Lasso – from sketch • Shooting algorithm (stochastic gradient descent) • At each iteration, randomly sample one dimension j, and update • How to deal with intercept • Center x and y • Standardize x • Tuning parameter • Shrinkage factor for a given • Convergence criterion

  6. Smoothing Parameter Selection • In data-rich settings, use training/validation/test sets for building models/selecting smoothing parameters/calculating prediction error. • Most situations are data-scarce, use approximations. • 1) Leave-one-out Cross validation (n-fold CV) • 2) K-fold Cross Validation • 3) Generalized Cross Validation • 4) Mallows Cp (not discussing)

  7. Piecewise Polynomials/Splines • Polynomials are good locally not globally. • Piecewise polynomials use this to model data locally in many regions (governed by knots) to approx. global fit. • Two main types of Splines: • Regression Splines • Smoothing Splines

  8. Piecewise Polynomials/Splines • Regression Splines = (# of knots) < (# of data points) • No regularization, fit by LS, nice linear smoother properties • But what order do we choose (linear, quadratic, cubic?) • How many knots and where to place them? • Hard questions!

  9. Piecewise Polynomials/Splines • Smoothing Splines: • Minimizing above quantity (for any function f(x)), leads to f(x) having a functional form of a NATURAL cubic spline w/ knots at every data point. • Natural cubic splines: Cubic regression splines (as talked about before + imposing linearity beyond the leftmost/rightmost knots). • The Nj basis functions are derived from the cubic regression spline basis functions.

  10. Piecewise Polynomials/Splines

  11. HW Tips • Follow Recitation code, if relevant • Plot coefficients scaled (for RR and Lasso) • For the shooting algorithm, utilize the WHILE loop when running it with a convergence criterion. • Check your algorithm by using the lars() function in R. • For the convergence criteria, make sure you take into account the history of beta updates and/or RSS updates. • (Note: | beta_(l) – beta_(l-1) | < TOL probably won’t cut it).

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