Lecture 9: Information Criteria
Learning Objectives
By the end of this lecture, you should be able to:
- Compute degrees of freedom for OLS/ridge/basis regression models
- Apply GCV formulas to select regularization parameters
- Identify nested models and when GCV/information criteria can and cannot be used for model selection
- Connect degrees of freedom to the bias-variance tradeoff
Overview and Motivation
Today is our last lecture on the bias-variance tradeoff for linear models (though this trade-off will come up again and again in this course). We’ve discussed a few methods for reducing risk, whose applicability depends on whether we’re in the high variance or high bias regime.
Techniques for reducing variance:
- Adding more training data
- Variable selection (manually removing predictors, LASSO regularization)
- Shrinkage (ridge regularization)
Techniques for reducing bias:
- Variable selection (manually adding predictors)
- Basis expansions (polynomial, splines, etc.)
Today, we’ll revisit the concept of model selection, the concluding topic of the first module, but this time with an eye towards the bias-variance tradeoff.
- We’ll learn a fun trick for computing the LOO-CV estimate of risk for OLS, ridge, and basis regression models without having to refit the model \(n\) times.
- We’ll also learn a new risk estimate, generalized cross-validation (GCV), which is rarely used in practice these days but which gives us a lot of insight into the bias-variance tradeoff.
Review: LOO-CV Formula For OLS
Recall that LOO-CV is an almost-unbiased estimate of the risk of a predictive model \(\mathcal R = \mathbb E[(Y - \hat{f}(X))^2]\) (where here we are assuming the use of the squared error loss).
Consider our OLS estimator:
\[ \hat{\beta}_{\text{OLS}} = (\boldsymbol{X}^\top \boldsymbol{X})^{-1} \boldsymbol{X}^\top \boldsymbol{Y} \]
where, again, \(\boldsymbol X\) and \(\boldsymbol Y\) are the concatenations of our training data \(\{ (X_i, Y_i) \}_{i=1}^n\):
\[ \boldsymbol{X} = \begin{bmatrix}--- & X_1^\top & --- \\ --- & X_2^\top & --- \\ & \vdots & \\ --- & X_n^\top & --- \end{bmatrix} \in \mathbb R^{n \times p} \quad \text{and} \quad \boldsymbol{Y} = \begin{bmatrix} Y_1 \\ Y_2 \\ \vdots \\ Y_n \end{bmatrix} \in \mathbb R^{n}. \]
Our OLS predictions on the training data are:
\[ \hat{\boldsymbol{Y}} = \boldsymbol{X} \hat{\beta}_{\text{OLS}} = \overbrace{\boldsymbol{X} (\boldsymbol{X}^\top \boldsymbol{X})^{-1} \boldsymbol{X}^\top}^{:= \boldsymbol H} \boldsymbol{Y} = \boldsymbol{H} \boldsymbol{Y}. \]
The matrix \(\boldsymbol H = \boldsymbol{X} (\boldsymbol{X}^\top \boldsymbol{X})^{-1} \boldsymbol{X}^\top\) is often referred to as the hat matrix, and it will be important in a second.
The LOO-CV estimate of risk involves, for each \(i = 1, \ldots, n\), computing the OLS estimator for the dataset \(\boldsymbol X_{-i}, \boldsymbol Y_{-i}\) (which is \(\boldsymbol X, \boldsymbol Y\) with the \(i^\mathrm{th}\) row removed, and make predictions on \(X_i\):
\[ \hat{Y}^{-i}(X_i) = X_i^\top \hat{\beta}_{\text{OLS}}^{-i} = X_i^\top (\boldsymbol{X}_{-i}^\top \boldsymbol{X}_{-i})^{-1} \boldsymbol{X}_{-i}^\top \boldsymbol{Y}_{-i}. \]
The Magic LOO-CV Formula for OLS (and Some Other Linear Models)
The LOO-CV estimate above requires \(n\) matrix inversions \((\boldsymbol{X}_{-i}^\top \boldsymbol{X}_{-i})^{-1}\), which is computationally expensive.
Moreover, the matrix inversion \((\boldsymbol{X}_{-i}^\top \boldsymbol{X}_{-i})^{-1}\) is not too different from the matrix inversion \((\boldsymbol{X}^\top \boldsymbol{X})^{-1}\) that we already computed to get \(\boldsymbol H\). Shouldn’t we be able to reuse that computation somehow?
It turns out that we can! With some ugly linear algebra, we can show that the LOO-CV predictions for OLS can be computed as:
\[ \hat{Y}^{-i}(X_i) = \frac{\hat{Y}_i - Y_i}{1 - H_{ii}}, \]
where \(\hat{Y}_i\) is the \(i^\mathrm{th}\) entry of \(\hat{\boldsymbol{Y}} = \boldsymbol{H} \boldsymbol{Y}\), and \(H_{ii}\) is the \(i^\mathrm{th}\) diagonal entry of the hat matrix \(\boldsymbol{H}\).
The derivation of this formula is tedious (and not for the faint of heart), but all it requires is some linear algebra. The key idea is to express
\[(\boldsymbol{X}_{-i}^\top \boldsymbol{X}_{-i})^{-1} = (\boldsymbol{X}^\top \boldsymbol{X} - X_i X_i^\top)^{-1}\]
and then use the Sherman-Morrison formula for inverting rank-one updates to a matrix. (Come see me in office hours if want to suffer through the details!)
- Applying this formula to estimate the risk associated with the square loss, the LOO-CV estimate of OLS risk is given by:
\[ \widehat{\mathcal{R}}_{\text{LOO}} = \frac{1}{n} \sum_{i=1}^n (Y_i - \hat{Y}^{-i}(X_i))^2 = \frac{1}{n} \sum_{i=1}^n \left( \frac{Y_i - \hat{Y}_i}{1 - H_{ii}} \right)^2. \]
This formula holds for any so-called linear smoother, where the predictions on the training data can be expressed as:
\[\hat{\boldsymbol{Y}} = \boldsymbol{H} \boldsymbol{Y} \quad \text{for some } \boldsymbol H \in \mathbb R^{n \times n}.\]
Here’s some examples of linear smoothers (and their corresponding \(\boldsymbol H\) matrices):
| Method | \(\boldsymbol{H}\) Matrix |
|---|---|
| OLS | \(\boldsymbol H = \boldsymbol{X} (\boldsymbol{X}^\top \boldsymbol{X})^{-1} \boldsymbol{X}^\top\) |
| Ridge Regression | \(\boldsymbol H = \boldsymbol{X} (\boldsymbol{X}^\top \boldsymbol{X} + \lambda \boldsymbol{I})^{-1} \boldsymbol{X}^\top\) |
| Basis Regression | \(\boldsymbol H = \widetilde{\boldsymbol{X}} (\widetilde{\boldsymbol{X}}^\top \widetilde{\boldsymbol{X}})^{-1} \widetilde{\boldsymbol{X}}^\top\) |
where the \(\widetilde{\boldsymbol{X}}\) matrix is the design matrix after applying basis expansions to the features; i.e.:
\[ \widetilde{\boldsymbol{X}} = \begin{bmatrix}--- & \phi(X_1)^\top & --- \\ --- & \phi(X_2)^\top & --- \\ & \vdots & \\ --- & \phi(X_n)^\top & --- \end{bmatrix} \in \mathbb R^{n \times d}, \]
where \(\phi: \mathbb R^p \to \mathbb R^d\) is the function that produces the basis expansion of a feature vector.
Generalized Cross-Validation (GCV)
To better understand the bias-variance tradeoff, it helps to simplify the LOO-CV formula above.
Consider what happens if we replace each \(H_{ii}\) with the average value of the diagonal entries of \(\boldsymbol H\):
\[ \frac{1}{n} \sum_{i=1}^n H_{ii} = \frac{1}{n} \text{trace}(\boldsymbol{H}) = \frac{\text{df}}{n}, \]
where \(\text{df} := \text{trace}(\boldsymbol{H})\) is called the degrees of freedom of the model.
The resulting estimate is called the generalized cross-validation (GCV) estimate of risk:
\[ \widehat{\mathcal{R}}_{\text{GCV}} = \frac{\frac{1}{n} \sum_{i=1}^n (Y_i - \hat{Y}_i)^2}{(1 - \text{df}/n)^2} = \frac{\mathrm{MSE}_\mathrm{train}}{(1 - \text{df}/n)^2}, \]
where \(\mathrm{MSE}_\mathrm{train} = \frac{1}{n} \sum_{i=1}^n (Y_i - \hat{Y}_i)^2\) is the mean squared error on the training set.
The degrees of freedom \(\text{df} = \text{trace}(\boldsymbol{H})\) has an intuitive interpretation as the “effective number of parameters” in the model.
| Method | Degrees of Freedom |
|---|---|
| OLS | \(\text{df} = \mathrm{tr}(\boldsymbol X ( \boldsymbol X^\top \boldsymbol X)^{-1} \boldsymbol X^\top) = p\) |
| Ridge Regression | \(\text{df} = \mathrm{tr}(\boldsymbol X ( \boldsymbol X^\top \boldsymbol X + \lambda \boldsymbol I)^{-1} \boldsymbol X^\top) = \sum_{i=1}^p \frac{d_i^2}{d_i^2 + \lambda} < p\) |
| Basis Regression | \(\text{df} = \mathrm{tr}(\widetilde{\boldsymbol X} ( \widetilde{\boldsymbol X}^\top \widetilde{\boldsymbol X})^{-1} \widetilde{\boldsymbol X}^\top) = d\) |
where \(d_1, \ldots, d_p\) are the singular values of \(\boldsymbol X\).
Derivation
Each of these can be derived using the singular value decomposition (SVD) of \(\boldsymbol X = \boldsymbol U \boldsymbol D \boldsymbol V^\top\) (or \(\widetilde{\boldsymbol X} = \widetilde{\boldsymbol U} \widetilde{\boldsymbol D} \widetilde{\boldsymbol V}^\top\) for basis regression). Let’s look at ridge regression:
\[\begin{align*} \text{df} &= \mathrm{tr}(\boldsymbol X ( \boldsymbol X^\top \boldsymbol X + \lambda \boldsymbol I)^{-1} \boldsymbol X^\top) \\ &= \mathrm{tr}(\boldsymbol U \boldsymbol D \boldsymbol V^\top (\boldsymbol V \boldsymbol D^\top \boldsymbol D \boldsymbol V^\top + \lambda \boldsymbol I)^{-1} \boldsymbol V \boldsymbol D^\top \boldsymbol U^\top) \\ &= \mathrm{tr}(\boldsymbol U \boldsymbol D (\boldsymbol D^\top \boldsymbol D + \lambda \boldsymbol I)^{-1} \boldsymbol D^\top \boldsymbol U^\top) \\ &= \mathrm{tr}(\boldsymbol D (\boldsymbol D^\top \boldsymbol D + \lambda \boldsymbol I)^{-1} \boldsymbol D^\top) \\ &= \sum_{i=1}^p \frac{d_i^2}{d_i^2 + \lambda} \end{align*}\](Note that the “degrees of freedom” concept exists for methods that don’t admit the closed-form \(\boldsymbol H\) matrix, like LASSO, but their derivation is PhD-level work.)
From the GCV estimate of risk, we can see how the bias-variance tradeoff plays out. There are two ways for \(\widehat{\mathcal{R}}_{\text{GCV}}\) to blow up:
- The training error \(\mathrm{MSE}_\mathrm{train}\) is large (causing the numerator to blow up), which happens when the predictive model is unable to fit the training data well (high bias).
- The degrees of freedom \(\text{df}\) is close to \(n\) (causing the denominator to \(\to 0\)), which happens when the predictive model has nearly as many “degrees of freedom” as training data (high variance).
The GCV estimate of risk is an example of an information criterion, which is a family of risk estimates that do not require a validation set or cross-validation. There are many others that you may encounter in the literature, including:
- Akaike Information Criterion (AIC)
- Bayesian Information Criterion (BIC)
- Mallows’ \(C_p\) (or Stein’s Unbiased Risk Estimate/SURE)
Each of these has its own derivation and assumptions, but they all are similar in spirit to GCV. They all involve a tradeoff between training error and model complexity (degrees of freedom); i.e. a bias-variance tradeoff.
What Happens to GCV When \(p \geq n\)?
When \(p = n\), note that our OLS model has \(\text{df} = p = n\), so the GCV estimate of risk is infinite!
When \(p > n\), OLS is no longer defined (because the matrix \(\boldsymbol{X}^\top \boldsymbol{X}\) is not invertible), so we can’t even compute the GCV estimate of risk.
Ridge regression can still be applied when \(p \geq n\). However, and importantly, its \(\text{df}\) does not exceed \(n\).
Why not?
- For ridge, we have that \(\text{df} = \sum_{i=1}^p \frac{d_i^2}{d_i^2 + \lambda}\), where \(d_1, \ldots, d_p\) are the (potentially zero) singular values of \(\boldsymbol X\).
- When \(p > n\), there are only \(n\) non-zero singular values, so there are only \(n\) non-zero terms in the sum.
- Each of the non-zero terms is strictly less than 1, so \(\text{df} < n\).
We’ll explore this last point a lot more in the next module!
Summary
- There’s a magical formula for computing LOO-CV risk estimates for OLS, ridge, and basis regression models without having to refit the model \(n\) times. Use it!
- The GCV estimate of risk is a simplified version of cross validation that (1) can be applied even when the “magic formula” doesn’t hold, and (2) does not require a validation set.
- The GCV estimate of risk makes the bias-variance tradeoff explicit: risk is high when training error is high (high bias) or when degrees of freedom is close to \(n\) (high variance).
- In general, CV/LOO-CV is preferred over GCV/information criteria for model selection, but GCV is a useful tool for understanding the bias-variance tradeoff.
- We will explore what happens when the number of degrees of freedom exceeds the number of training samples in the next lecture!