Glossary of Terms

Abstract

To help readers understand common terms in machine learning, statistics, and data mining, we provide a glossary of common terms.The definitions are not designed to be completely general, but instead are aimed at the most common case. Accuracy (error rate)The rate of correct (incorrect) predictions made by the model over a data set (cf. coverage).Accuracy is usually estimated by using an independent test set that was not used at any time during the learning process.More complex accuracy estimation techniques, such as cross-validation and the bootstrap, are commonly used, especially with data sets containing a small number of instances. Association learningTechniques that find conjunctive implication rules of the form "X and Y → A and B" (associations) that satisfy given criteria.The conventional association algorithms are sound and complete methods for finding all associations that satisfy criteria for minimum support (at least a specified fraction of the instances must satisfy both sides of the rule) and minimum confidence (at least a specified fraction of instances satisfying the left hand side, or antecedent, must satisfy the right hand side, or consequent).Attribute (field, variable, feature) A quantity describing an instance.An attribute has a domain defined by the attribute type, which denotes the values that can be taken by an attribute.The following domain types are common:Categorical A finite number of discrete values.The type nominal denotes that there is no ordering between the values, such as last names and colors.The type ordinal denotes that there is an ordering, such as in an attribute taking on the values low, medium, or high.Continuous (quantitative) Commonly, subset of real numbers, where there is a measurable difference between the possible values.Integers are usually treated as continuous in practical problems.