OneR is a simple algorithm that simply predicts the class of a sample by finding the most frequent class for the feature values. OneR is shorthand for One Rule, indicating we only use a single rule for this classification by choosing the feature with the best performance. While some of the later algorithms are significantly more complex, this simple algorithm has been shown to have good performance in some real-world datasets.
The algorithm starts by iterating over every value of every feature. For that value, count the number of samples from each class that has that feature value. Record the most frequent class of the feature value, and the error of that prediction.
For example, if a feature has two values, ...
