Class Ai4r::Classifiers::OneR
In: lib/ai4r/classifiers/one_r.rb
Parent: Classifier

Introduction

The idea of the OneR algorithm is identify the single attribute to use to classify data that makes fewest prediction errors. It generates rules based on a single attribute.

Methods

build   build_rule   eval   get_rules  

Attributes

data_set  [R] 
rule  [R] 

Public Instance methods

build(data_set)

Build a new OneR classifier. You must provide a DataSet instance as parameter. The last attribute of each item is considered as the item class.

eval(data)

You can evaluate new data, predicting its class. e.g. 

  classifier.eval(['New York',  '<30', 'F'])  # => 'Y'
get_rules()

This method returns the generated rules in ruby code. e.g. 

  classifier.get_rules
    # =>  if age_range == '<30' then marketing_target = 'Y'
          elsif age_range == '[30-50)' then marketing_target = 'N'
          elsif age_range == '[50-80]' then marketing_target = 'N'
          end

It is a nice way to inspect induction results, and also to execute them: 

    marketing_target = nil
    eval classifier.get_rules
    puts marketing_target
      # =>  'Y'

Protected Instance methods

[Validate]