One Against All (oaa) multi class example

Overview

OAA stands for "One Against All".

Purpose:

The option --oaa <K> where is the number of distinct classes directs vw to perform K multi-class (as opposed to binary) classification.

Note:

• Data-set labels must be in the natural number set {1 .. <K>}
• <K> is the maximum label value, and must be passed as an argument to --oaa

Implementation of the reduction:

• Uses a loop of K separate binary classifications.
• Each iteration classifies target feature i of K against all others (binary classification).

Example

Assume we have a 3-class classification problem. We label our 3 classes {1,2,3}

Our data set oaa.dat may look like this

1 ex1| a
2 ex2| a b
3 ex3| c d e
2 ex4| b a
1 ex5| f g

This is essentially the same format as the non multi-class case (classification or regression) where each label must belong to one of the {1..<K>} classes, i.e. a natural number between 1 and <K>. You may add weights to the example and the features, use name-spaces, etc.

We train:

vw --oaa 3 oaa.dat -f oaa.model

Which gives this progress output:

final_regressor = oaa.model
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading from oaa.dat
num sources = 1
average    since         example     example  current  current  current
loss       last          counter      weight    label  predict features
0.666667   0.666667          3      3.0          3        1        4

finished run
number of examples = 5
weighted example sum = 5
weighted label sum = 0
average loss = 0.4
best constant = 0
total feature number = 15

Now we can predict, using the same data set as our test-set:

vw -t -i oaa.model oaa.dat -p oaa.predict

Similar to what we do in vanilla classification or regression. The resulting oaa.predict file has contents:

1.000000 ex1
2.000000 ex2
3.000000 ex3
2.000000 ex4
1.000000 ex5

Which is as expected: 'ex1' and 'ex5' belong to class 1, 'ex2' and 'ex4' belong to class 2, and 'ex3' belongs to class 3.