OneR, short for "One Rule", is an easy classification algorithm that generates one rule for each predictor in the data, then selects the rule with the smallest total error as its "one rule". OneR is known to often beat more sophisticated algorithms and is easy to implement and understand due to it's simplicity.
To create a rule for a predictor, we construct a frequency table for each predictor against the target.
