# Probability Estimation: Bayes Risk

In my previous post on probability estimation, I introduced the notion of a proper loss. This is a way of assigning penalties to probability estimates so that the average loss is minimised by guessing the true conditional probability of a positive label for each example. This minimal possible risk is called the (conditional) Bayes risk and in this post I will highlight some of its properties.

To recap briefly, we denote the loss of predicting the probability $p$ when the label $y$ (1 for positive, 0 for negative) as $\ell(y, p)$. Then the conditional risk for $\ell$ of guessing $p$ when $y$ has probability $\eta$ of being positive is $L(\eta,p) = (1-\eta)\,\ell(0,p) + \eta\,\ell(1,p).$

## Point-wise Bayes Risk

The best possible estimate under this loss in terms of minimising the risk at when the probability of a positive label is $\eta$ is the (point-wise) Bayes risk at $\eta$, which I will denote as $L^*(\eta) = \min_{p \in [0,1]} L(\eta, p).$

As argued in the previous post, a sensible loss is one that is Fisher consistent, that is, one with a risk that is minimised when $p=\eta$. Such a loss is called proper and its risk and Bayes risk are closely related. Specifically, $L^*(\eta) = L(\eta,\eta)$.

This relationship makes it trivial to compute the point-wise Bayes risk for any proper loss. For example, square loss is defined to be $\ell_{\text{sq}}(y,p) = y\,(1-p)^2 + (1-y)\,p^2$ and so its point-wise Bayes risk is $L^*_{\text{sq}}(\eta) = L_{\text{sq}}(\eta,\eta) = \eta(1-\eta)^2 + (1-\eta)\eta^2 = \eta(1-\eta).$

Log loss is $\ell_{\text{log}}(y,p) = -y\log(p) - (1-y)\log(1-p)$ and so its Bayes risk is $L^*_{\text{log}}(\eta) = -\eta\log(\eta) - (1-\eta)\log(1-\eta).$

## Bayes Risk Functions are Concave

One useful property of point-wise Bayes risk functions for proper losses is that they are necessarily concave. That is, a line joining any two points on the graph of $L^*$ lies entirely below $L^*$.

The quickest way to establish this is via a well-known result regarding concave functions is that the point-wise minimum of a set of concave functions is concave.1 Then, for note that for any fixed $p\in[0,1]$ the function $L(\eta,p)$ is linear in $\eta$ since the terms $\ell(1,p)$ and $\ell(0,p)$ are constant. Since linear functions are concave and, by definition, $L^*$ is their point-wise minimum we see that $L^*$ must also be concave.

Concave functions have many useful properties that have implications for the study of point-wise risks. Firstly, they are necessarily continuous, and secondly, if they are twice differentiable, their second derivatives are non-positive. That is, for all $\eta$, $(L^*)''(\eta) \leq 0$

which also implies that their first derivatives are monotonically decreasing.2

As we will see in the next post, the converse of this holds too. That is, each concave function on $[0,1]$ can be interpreted as the point-wise Bayes risk for some proper loss.

1. See, for example, §3.2.3 of Boyd & Vandenberghe’s freely available book Convex Optimization.

2. You can easily check this is the case for the log- and square-losses.

March 5, 2009 Canberra, Australia
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