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Lecture 18. Regularization in Regression Analysis (regularization, penalization)

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1. Overview

2. MSPE

3. Technique 1. Ridge regression

4. Technique 2. Lasso regression

5. Technique 3. Elastic net

6. Technique 4. SelectFromModel

**1. Overview **

⑴ Problems in regression analysis: Mainly prominent when there are many regression variables

① Multicollinearity

② Underfitting: The model lacks flexibility and cannot properly learn the given data

③ Overfitting

○ In standard regression like OLS estimation, the model learns the noise in the sample, reducing predictive power

○ Learning the bias during training can actually improve predictive power

⑵ Regularization (penalization)

① To solve regression problems, a penalty term is added to the parameters

② Note that not applying regularization causes overfitting, while too much causes underfitting

③ Standardization of data must be performed

○ Features with large values have large coefficients, which may be overly penalized and shrink too much

○ Conversely, features with small values have small coefficients and may be under-penalized

④ Sometimes includes a process of optimizing parameters (e.g. penalty term weight) using a validation set

⑤ Expected results of regularization

Figure 1. Expected results of regularization

2. MSPE

⑴ Overview

① Error: Assuming e is squared error, h is hypothesis, and f is true function

② Type 1. In-sample error: Also called training error. Similar to bias

③ Type 2. Out-of-sample error: Also called generalization error, MSPE. Similar to variance

○ Step 1. Build prediction model using given sample

○ Step 2. Compare predicted and actual values using data outside the sample (XOOS, YOOS)

○ Note: ŷ refers to prediction obtained using in-sample data

④ (Reference) bias-variance tradeoff

⑤ Best prediction quantity: Called oracle predictor. E(YOOS

XOOS)

○ Prediction error in MSPE is as follows

○ Fundamental error: Cannot be improved. YOOS - E(YOOS

XOOS)

○ Estimation error: Ŷ(XOOS) - E(YOOS

XOOS)

⑵ MSPE estimator

① If β is known, MSPE = σu2 holds

② k/n may be large

⑶ Assumptions

① Assumption 1. No multicollinearity

② Assumption 2. (XOOS, YOOS) are randomly drawn from the same population

⑷ Transformations

① Standardization

○ (Xi1, ···, Xki, Yi*) are values extracted from original sample

○ Define Xji as (Xji* - μXj) / σXj

○ Dependent variable is transformed as Yσj ← Yσj - μY*

② Principle of shrinkage

○ Can reduce MSPE

○ Bias occurs instead: Tradeoff

○ Most famous example is James-Stein estimator

⑸ In-sample MSPE calculation: m-fold cross validation is commonly used

① 1^st. Split given sample into m parts

② 2^nd. Use m-1 parts to estimate parameters: Training data

③ 3^rd. Use the remaining part to evaluate performance: Testing data

④ 4^th. Repeat m times with different combinations

⑤ 5^th. Take average to determine final estimator

⑥ Typically 10-fold cross validation is used

⑹ Out-of-sample root MSPE calculation

① Use model trained on in-sample data to evaluate performance on different sample

② This different sample is called validation set

3. Technique 1. Ridge regression

⑴ Overview

① Definition: Penalizes squared values to control model complexity. Penalty is a function of weights

② Also called L2 regularization

③ Introduced in 1962 by A. E. Hoerl to solve non-invertibility of the regression matrix

④ MAP learning for Gaussian distribution

⑵ Objective function

① Simple form

② PRSS (penalized residual sum of squares)

⑶ Case 1. Regression variables are uncorrelated

① Simple form: Can express relative to β̂j found when λ = 0

② Matrix form: Ridge objective function is convex, allowing easy solution via differentiation

⑷ Case 2. Regression variables are correlated: Must examine MSPE with respect to λ_Ridge

① Bias-variance trade-off

Figure 2. General bias-variance trade-off

② λ_Ridge is calculated via cross validation

③ λ_Ridge = 0 fits best in-sample but not out-of-sample

Figure 3. Square root of MSPE according to λ_Ridge

⑸ Characteristics of Ridge regression solution

① Even without invertible XtX, λ allows calculation of inverse

② Each λ gives one estimator

③ λ → 0: Overfitting. Reaches linear regression (OLS) solution

④ λ → ∞: Underfitting. Coefficients w approach 0 (∵ penalty on large coefficients)

① Standard gradient descent: w t - η∇Ein(w t)

⑻ Application 3. MAP (maximum a posteriori)

① Bayes rule

② General MAP learning: Recall case where “P(D) = constant” in Bayes rule

○ Assuming normal distribution: Assuming w is unrelated to prior except w0 and is small

③ MAP learning in Ridge regression

⑼ Application 4. Comparison with other methods

Figure 4. Comparison of predictive performance

## 4. Technique 2. Lasso regression

⑴ Overview

① Definition: Penalizes absolute values to control model complexity. Penalty is a function of weights

② Also called L1 regularization

③ MAP learning for Laplacian prior

Figure 5. Laplace probability density function

⑵ Objective function

① Simple form

② Matrix form

⑶ Solution of objective function: Compute MSPE with respect to λ_Lasso

Figure 6. Square root of MSPE according to λ_Lasso

① λ_Lasso is calculated via cross validation

② Unlike Ridge, no general closed-form solution

⑷ Characteristics

① Useful when model has sparsity property: i.e., many coefficients are 0

② λ → 0: Reaches linear regression (OLS) solution. Best in-sample but poor out-of-sample

③ λ → ∞: Coefficients w approach 0 (∵ penalty on large coefficients)

⑸ Application 1. Principle of sparsity

① Laplace prior sets unimportant variables exactly to 0: Effectively removes unimportant variables

Figure 7. Changes in coefficients with shrinkage factor

② Diagram of sparsity principle

Figure 8. Intuitive understanding of Lasso regression’s sparsity

○ Red ellipses connect points of equal MSE (mean squared error)

○ Blue region connects points with equal penalty

○ As λ increases, penalty increases, both Lasso and Ridge shrink

○ In Ridge, optimum occurs at point where red ellipse touches circular blue region: If not, solution lies nearer to origin with smaller penalty

○ In Lasso, if blue region is small, optimal solution occurs at point where some coefficients are 0: At this sharp point, movement along edge exits red ellipse (→ higher MSE)

○ Unlike Ridge, Lasso induces sparsity

⑹ Application 2. Comparison with other methods

Figure 9. Comparison of predictive performance

5. Technique 3. Elastic Net

⑴ A linear combination of lasso and ridge. Adds both sum of absolute values and squared values of weights as penalty terms

⑵ Parameter 1. Alpha (α): Controls mix ratio of L1 and L2 penalties. α = 1 is Lasso, α = 0 is Ridge

⑶ Parameter 2. Lambda (λ): Controls strength of penalty. Multiplies the entire regularization term

6. Technique 4. SelectFromModel

⑴ A method for selecting variables based on decision tree algorithms

Input: 2019.12.08 12:35

Edit: 2024.09.27 08:47