one & zero attribute rule

One-attribute-rule
The one-attribute-rule, or OneR, is an algorithm for finding association rules. According to Ross, very simple association rules, involving just one attribute in the condition part, often work well in practice with real-world data.[17]. The idea of the OneR (one-attribute-rule) algorithm is to find the one attribute to use to classify a novel datapoint that makes fewest prediction errors.

For example, to classify a car you haven't seen before, you might apply the following rule: If Fast Then Sportscar, as opposed to a rule with multiple attributes in the condition: If Fast And Softtop And Red Then Sportscar.

The algorithm is as follows:

For each attribute A:
For each value V of that attribute, create a rule:
1. count how often each class appears
2. find the most frequent class, c
3. make a rule "if A=V then C=c"
Calculate the error rate of this rule
Pick the attribute whose rules produce the lowest error rate

[edit]Zero-attribute-rule
The zero-attribute-rule, or ZeroR, does not involved any attribute in the condition part, and always returns the most frequent class in the training set. This algorithm is frequently used to measure the classification success of other algorithms.

in reference to: HimmiH (view on Google Sidewiki)