## Bayesian Classification

• A form of machine learning

• Given a bunch of evidence for and against a thing, decide whether the thing

• Formulate as a classic probability problem

 pr(H|E1=v1, E2=v2, ... En=vn)

• We can use Bayes Rule to turn this around

   pr(E1=v1, E2=v2, ... En=vn|H) pr(H)
= -----------------------------------
pr(E1=v1, E2=v2, ... En=vn)

• Now we "just" need the probabilities: perhaps we can examine a bunch of classified examples and compute them

## Binary Naïve Bayes

• Problem: There's a huge space of possible values here; likely no way we'll get enough examples to accurately estimate probabilities

• Let's reduce the state space:

• Binarize the hypothesis and evidence. Now the state space size is 2^(n+1) for n features

• Naively assume that all the features are independent (they aren't). Then we can just replace the conjunction with a product

   pr(E1=v1|H) pr(E2=v2|H) ... pr(En=vn|H) pr(H)
= ---------------------------------------------
pr(E1=v1) pr(E2=v2) ... pr(En=vn)

• Sadly, the naive assumption means that the probabilities will be too low, since interactions won't be counted: but comparable for H true and false. So

H iff pr(H|E) > pr(not H|E)

• Note that this comparison drops the denominator, which is nice

• Games might be played to make the computation more tractable

## m-Estimation

• What we have so far:

                    pr(E1=v1|H) ... pr(En=vn|H) pr(H)
pr(H|E1=v1, ...) = ---------------------------------
pr(E1=v1) ... pr(En=vn)

• Consider the case where pr(Ei=vi|H)=0. If we try to classify an instance of this form, this will make the whole product 0, regardless of what the other terms tell us

• (Worse yet, consider pr(Ei=vi)=0. Oops. We drop the denominator in the comparison, but…)

• The training instances are sampled from a notionally large space. If we see no count in n training instances, that means either that the frequency is less than 1/n or we got unlucky

• Assume that there is some fraction m associated with the undersampling — usually use ½

              |S[Ei=vi|H]+m|
pr(Ei=vi|H) = --------------
|S[H]+m|

• Zeros are gone, calculation may be more accurate

## Numerics

• We are asked to compute a product of potentially many terms:

pr(E1=v1|H) ... pr(En=vn|H) pr(H)

• All terms are ≤ 1

• When n is large, this product will be small. Maybe too small. Floating point underflow could happen

• We are comparing

pr(E1=v1|H) ... pr(En=vn|H) pr(H) >
pr(E1=v1|not H) ... pr(En=vn|not H) pr(not H) ?

• Take log on both sides:

log(pr(E1=v1|H) ... pr(En=vn|H) pr(H)) >
log(pr(E1=v1|not H) ... pr(En=vn|not H) pr(not H)) ?

log(pr(E1=v1|H)) +  ... + log(pr(En=vn|H) + log(pr(H))) >
log(pr(E1=v1|not H)) +  ... + log(pr(En=vn|not H) + log(pr(not H))) ?

• These sums should not underflow

## Heart Anomalies: The HW2 Dataset

• We have a bunch of evidence of heart anomalies obtained from radiography

• The evidence consists of a fixed list of features that are present in each radiogram

• Further, the radiograms have been accurately classified into one of two categories: normal or abnormal

• Imagine we are given a radiogram of unknown category but with all the features measured

• Our job: Is this a radiogram of a normal or abnormal heart?

• See Homework 2