16 Logistic regression

Stat 406

Geoff Pleiss, Trevor Campbell

Last modified – 16 October 2024

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Last time

  • We showed that with two classes, the Bayes’ classifier is

\[g_*(X) = \begin{cases} 1 & \textrm{ if } \frac{p_1(X)}{p_0(X)} > \frac{1-\pi}{\pi} \\ 0 & \textrm{ otherwise} \end{cases}\]

where

  • \(p_1(X) = \P(X \given Y=1)\)
  • \(p_0(X) = \P(X \given Y=0)\)
  • \(\pi = \P(Y=1)\)

This lecture

How do we estimate \(p_1(X), p_0(X), \pi\)?

Warmup: estimating \(\pi = Pr(Y=1)\)

A good estimator:

\[ \hat \pi = \frac{1}{n} \sum_{i=1}^n 1_{y_i = 1} \]

(I.e. count the number of \(1\)s in the training set)

This estimator is low bias/variance.


As we will soon see, it turns out we won’t have to use this estimator.

Estimating \(p_0\) and \(p_1\) is much harder

\(\P(X=x \mid Y = 1)\) and \(\P(X=x \mid Y = 0)\) are \(p\)-dimensional distributions.
Remember the curse of dimensionality?


Can we simplify our estimation problem?

Sidestepping the hard estimation problem

Rather than estimating \(\P(X=x \mid Y = 1)\) and \(\P(X=x \mid Y = 0)\), I claim we can instead estimate the simpler ratio

\[ \frac{\P(Y = 1 \mid X=x)}{\P(Y = 0 \mid X=x)} \] Why?


Recall that \(g_*(X)\) ony depends on the ratio \(\P(X \mid Y = 1) / \P(X \mid Y = 0)\).

\[ \begin{align*} \frac{\P(X=x \mid Y = 1)}{\P(X=x \mid Y = 0)} &= \frac{ \tfrac{\P(Y = 1 \mid X=x) \P(X=x)}{\P(Y = 1)} }{ \tfrac{\P(Y = 0 \mid X=x) \P(X=x)}{\P(Y = 0)} } = \frac{\P(Y = 1 \mid X=x)}{\P(Y = 0 \mid X=x)} \underbrace{\left(\frac{1-\pi}{\pi}\right)}_{\text{Easy to estimate with } \hat \pi} \end{align*} \]

Direct model

As with regression, we’ll start with a simple model.
Assume our data can be modelled by a distribution of the form

\[ \log\left( \frac{\P(Y = 1 \mid X=x)}{\P(Y = 0 \mid X=x)} \right) = \beta_0 + \beta^\top x \]

Why does it make sense to model the log ratio rather than the ratio?

From this eq., we can recover an estimate of the ratio we need for the Bayes classifier:

\[ \begin{align*} \log\left( \frac{\P(X=x \mid Y = 1)}{\P(X=x \mid Y = 0)} \right) &= \log\left( \frac{\tfrac{\P(X=x)}{\P(Y = 1)}}{\tfrac{\P(X=x)}{\P(Y = 0)}} \right) + \log\left( \frac{\P(Y = 1 \mid X=x)}{\P(Y = 0 \mid X=x)} \right) \\ &= \underbrace{\left( \tfrac{1 - \pi}{\pi} + \beta_0 \right)}_{\beta_0'} + \beta^\top x \end{align*} \]

Recovering class probabilities

\[ \text{Our model:}\qquad \log\left( \frac{\P(Y = 1 \mid X=x)}{\P(Y = 0 \mid X=x)} \right) = \beta_0 + \beta^\top x \]


We know that \(\P(Y = 1 \mid X=x) + \P(Y = 0 \mid X=x) = 1\). So…

\[ \frac{\P(Y = 1 \mid X=x)}{\P(Y = 0 \mid X=x)} = \frac{\P(Y = 1 \mid X=x)}{1 - \P(Y = 1 \mid X=x)} = \exp\left( \beta_0 + \beta^\top x \right), \]

\[ \frac{\P(Y = 1 \mid X=x)}{\P(Y = 0 \mid X=x)} = \frac{\P(Y = 1 \mid X=x)}{1 - \P(Y = 1 \mid X=x)} = \exp\left( \beta_0 + \beta^\top x \right), \]

After algebra… \[ \begin{aligned} \P(Y = 1 \given X=x) &= \frac{\exp\{\beta_0 + \beta^{\top}x\}}{1 + \exp\{\beta_0 + \beta^{\top}x\}}, \\\ \P(Y = 0 | X=x) &= \frac{1}{1 + \exp\{\beta_0 + \beta^{\top}x\}} \end{aligned} \]

This is logistic regression.

Logistic Regression

\[\P(Y = 1 \given X=x) = \frac{\exp\{\beta_0 + \beta^{\top}x\}}{1 + \exp\{\beta_0 + \beta^{\top}x\}} = h\left( \beta_0 + \beta^\top x \right),\]

where \(h(z) = (1 + \exp(-z))^{-1} = \exp(z) / (1+\exp(z))\) is the logistic function.


The “logistic” function is nice.

  • It’s symmetric: \(1 - h(z) = h(-z)\)

  • Has a nice derivative: \(h'(z) = \frac{\exp(z)}{(1 + \exp(z))^2} = h(z)(1-h(z))\).

  • It’s the inverse of the “log-odds” (logit): \(\log(p / (1-p))\).

Logistic regression is a Linear Classifier

Logistic regression is a linear classifier

\[\log\left( \frac{\P(Y = 1 \given X=x)}{\P(Y = 0 \given X=x) } \right) = \beta_0 + \beta^{\top} x\]

  • If the log-odds are \(>0\), classify as 1
    (\(Y=1\) is more likely)

  • If the log-odds are \(<0\), classify as a 0
    (\(Y=0\) is more likely)


The decision boundary is the hyperplane \(\{x : \beta_0 + \beta^{\top} x = 0\}\)

Visualizing the classification boundary

Code 
library(mvtnorm)
library(MASS)
generate_lda_2d <- function(
    n, p = c(.5, .5),
    mu = matrix(c(0, 0, 1, 1), 2),
    Sigma = diag(2)) {
  X <- rmvnorm(n, sigma = Sigma)
  tibble(
    y = which(rmultinom(n, 1, p) == 1, TRUE)[, 1],
    x1 = X[, 1] + mu[1, y],
    x2 = X[, 2] + mu[2, y]
  )
}

dat1 <- generate_lda_2d(100, Sigma = .5 * diag(2))
logit <- glm(y ~ ., dat1 |> mutate(y = y - 1), family = "binomial")

gr <- expand_grid(x1 = seq(-2.5, 3, length.out = 100), 
                  x2 = seq(-2.5, 3, length.out = 100))
pts <- predict(logit, gr)
g0 <- ggplot(dat1, aes(x1, x2)) +
  scale_shape_manual(values = c("0", "1"), guide = "none") +
  geom_raster(data = tibble(gr, disc = pts), aes(x1, x2, fill = disc)) +
  geom_point(aes(shape = as.factor(y)), size = 4) +
  coord_cartesian(c(-2.5, 3), c(-2.5, 3)) +
  scale_fill_steps2(n.breaks = 6, name = "log odds") 
g0

Linear classifier \(\ne\) linear smoother

  • While logistic regression produces linear decision boundaries, it is not a linear smoother

  • AIC/BIC/Cp work if you use the likelihood correctly and count degrees-of-freedom correctly

    • \(\mathrm{df} = p + 1\) (Why?)

  • Most people use CV

Logistic regression in R

logistic <- glm(y ~ ., dat, family = "binomial")

Or we can use lasso or ridge regression or a GAM as before

lasso_logit <- cv.glmnet(x, y, family = "binomial")
ridge_logit <- cv.glmnet(x, y, alpha = 0, family = "binomial")
gam_logit <- gam(y ~ s(x), data = dat, family = "binomial")

Estimating \(\beta_0\), \(\beta\)

For regression…

\[ \text{Model:} \qquad \P(Y=y \mid X=x) = \mathcal N( y; \:\: \beta^\top x, \:\:\sigma^2) \]

… recall that we motivated OLS with the principle of maximum likelihood

\[ \begin{align*} \hat \beta_\mathrm{OLS} &= \argmax_{\beta} \prod_{i=1}^n \P(Y_i = y_i \mid X_i = x_i) \\ &= \argmin_{\beta} \sum_{i=1}^n -\log\P(Y_i = y_i \mid X_i = x_i) \\ \\ &= \ldots (\text{because regression is nice}) \\ &= \textstyle \left( \sum_{i=1}^n x_i x_i^\top \right)^{-1}\left( \sum_{i=1}^n y_i x_i \right) \end{align*} \]

Estimating \(\beta_0\), \(\beta\)

For classification with logistic regression…

\[ \begin{align*} \text{Model:} &\qquad \tfrac{\P(Y=1 \mid X=x)}{\P(Y=0 \mid X=x)} = \exp\left( \beta_0 +\beta^\top x \right) \\ \text{Or alternatively:} &\qquad \P(Y=1 \mid X=x) = h\left(\beta_0 + \beta^\top x \right) \\ &\qquad \P(Y=0 \mid X=x) = h\left(-(\beta_0 + \beta^\top x)\right) \end{align*} \] … we can also apply the principle of maximum likelihood

\[ \begin{align*} \hat \beta_{0}, \hat \beta &= \argmax_{\beta_0, \beta} \prod_{i=1}^n \P(Y_i \mid X_i) \\ &= \argmin_{\beta_0,\beta} \sum_{i=1}^n -\log\P(Y_i \mid X_i) \end{align*} \]

Unfortunately that’s as far as we can get with algebra alone.

Next time:

The workhorse algorithm for obtaining \(\hat \beta_0\), \(\hat \beta\)

UBC Stat 406 - 2024