A common practice in data mining is to classify, to look at the attributes of an object or situation and make a guess at what category the observed item belongs to.
As new evidence is examined (typically by feeding a training set to a learning algorithm), these guesses are refined and improved.
While classifiers read a collection of data and collect information that is used to place new data into a series of discrete categories, contrast set learning takes the category that an item belongs to and attempts to reverse engineer the statistical evidence that identifies an item as a member of a class.
That is, contrast set learners seek rules associating attribute values with changes to the class distribution.
Measurements would be taken at regular intervals throughout the launch, noting factors such as the trajectory of the rocket, operating temperatures, external pressures, and so on.
[2] Association rule learners typically offer rules linking attributes commonly occurring together in a training set (for instance, people who are enrolled in four-year programs and take a full course load tend to also live near campus).
[3] For example, a contrast set learner could ask, “What are the key identifiers of a person with a bachelor's degree or a person with a PhD, and how do people with PhD's and bachelor’s degrees differ?” Standard classifier algorithms, such as C4.5, have no concept of class importance (that is, they do not know if a class is "good" or "bad").
Several contrast set learners, such as MINWAL[4] or the family of TAR algorithms,[5][6][7] assign weights to each class in order to focus the learned theories toward outcomes that are of interest to a particular audience.
In the following small dataset, each row is a supermarket transaction and each "1" indicates that the item was purchased (a "0" indicates that the item was not purchased): Given this data, Treatment learning is a form of weighted contrast-set learning that takes a single desirable group and contrasts it against the remaining undesirable groups (the level of desirability is represented by weighted classes).
Children are added by specializing the set with additional items picked through a canonical ordering of attributes (to avoid visiting the same nodes twice).
Incorrect or misleading data noise, if correlated with failing examples, may result in an overfitted rule set.
Such an overfitted model may have a large lift score, but it does not accurately reflect the prevailing conditions within the dataset.
If no improvement is seen after a user-defined number of rounds, the algorithm terminates and returns the top-scoring rule sets.