**Introduction**

In one of the earliest posts to this log, Introduction To Entropy (Nov-10-2006), I described the entropy of discrete variables. Finally, in this posting, I have gotten around to continuing this line of inquiry and will explain

*conditional entropy*.

**Quick Review: Entropy**

Recall that

*entropy*is a measure of uncertainty about the state of a variable. In the case of a variable which can take on only two values (male/female, profit/loss, heads/tails, alive/dead, etc.), entropy assumes its maximum value, 1 bit, when the probability of the outcomes is equal. A fair coin toss is a high-entropy event: Beforehand, we have no idea about what will happen. As the probability distribution moves away from a 50/50 split, uncertainty about the outcome decreases since there is less uncertainty as to the outcome. Recall, too, that entropy decreases regardless of which outcome class becomes the more probable: an unfair coin toss, with a heads / tails probability distribution of 0.10 / 0.90 has exactly the same entropy as another unfair coin with a distribution of 0.90 / 0.10. It is the distribution of probabilities, not their order which matters when calculating entropy.

Extending these ideas to variables with more than 2 possible values, we note that generally, distributions which are evenly spread among the possible values exhibit higher entropy, and those which are more concentrated in a subset of the possible values exhibit lower entropy.

**Conditional Entropy**

Entropy, in itself, is a useful measure of the mixture of values in variables, but we are often interested in characterizing variables after they have been conditioned ("modeled").

*Conditional entropy*does exactly that, by measuring the entropy remaining in a variable, after it has been conditioned by another variable. I find it helpful to think of conditional entropy as "residual entropy". Happily for you, I have already assembled a MATLAB function, ConditionalEntropy, to calculate this measure.

As an example, think of sporting events involving pairs of competitors, such as soccer ("football" if you live outside of North America). Knowing nothing about a particular pair of teams (and assuming no ties), the best probability we may assess that any specific team, say, the Black Eagles (Go, Beşiktaş!), will win is:

p(the Black Eagles win) = 0.5

The entropy of this variable is 1 bit- the maximum uncertainty possible for a two-category variable. We will attempt to lower this uncertainty (hence, lowering its conditional entropy).

In some competitive team sports, it has been demonstrated statistically that home teams have an advantage over away teams. Among the most popular sports, the home advantage appears to be largest for soccer. One estimate shows that (excluding ties) home teams have historically won 69% of the time, and away teams 31% of the time. Conditioning the outcome on home vs. away, we may provide the follwing improved probability estimates:

p(the Black Eagles win, given that they are the home team) = 0.69

p(the Black Eagles win, given that they are the away team) = 0.31

Ah, Now there is somewhat less uncertainty! The entropy for probability 0.69 is 0.8932 bits. The entropy for probability 0.31 (being the same distance from 0.5 as 0.69) is also 0.8932 bits.

Conditional entropy is calculated as the weighted average of the entropies of the various possible conditions (in this case home or away). Assuming that it is equally likely that the Black Eagles play home or away, the conditional entropy of them winning is 0.8932 bits = (0.5 * 0.8932 + 0.5 * 0.8932). As entropy has gone down with our simple model, from 1 bit to 0.8932 bits, we learn that knowing whether the Black Eagles are playing at home or away provides information and reduces uncertainty.

Other variables might be used to condition the outcome of a match, such as number of player injuries, outcome of recent games, etc. We can compare these candidate predictors using conditional entropy. The lower the conditional entropy, the lower the remaining uncertainty. It is even possible to assess a combination of predictors by treating each combination of univariate conditions as a separate condition ("symbol", in information theoretic parlance), thus:

p(the Black Eagles win, given:

that they are the home team and there are no injuries) = ...

that they are the away team and there are no injuries) = ...

that they are the home team and there is 1 injury) = ...

that they are the away team and there is 1 injury) = ...

etc.

My routine,

*ConditionalEntropy*can accommodate multiple conditioning variables.

**Conclusion**

The models being developed here are tables which simplistically cross all values of all input variables, but conditional entropy can also, for instance, be used to evaluate candidate splits in decision tree induction, or to assess class separation in discriminant analysis.

Note that, as the entropies calculated in this article are based on

**sample**probabilities, they suffer from the same limitations as all sample statistics (as opposed to population statistics). A sample entropy calculated from a small number observations likely will not agree exactly with the population entropy.

**Further Reading**

See also my Sep-12-2010 posting, Reader Question: Putting Entropy to Work.

Print:

*The Mathematical Theory of Communication*by Claude Shannon (ISBN 0-252-72548-4)

*Elements of Information Theory*by Cover and Thomas (ISBN 0-471-06259)

## 4 comments:

Your explaination is very helpful! keep up with your good work!

Nice post - very informative and easy to understand

Why is it not possible to calculate mutual information of two datasets with different number of elements (different dimension)?

Thanks.

Thanks a lot,

If I could make a wish, I'd very much like to see a post about how Entropy is used with continuous variables. That would be very interesting!

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