Oracle Advanced Analytic's provides a broad range of in-database, parallelized implementations of machine learning algorithms to solve many types of business problems. See Oracle Advanced Analytics Documentation for more information and details on each algorithm, settings and API calls. See DBMS_DATA_MINING in Database PL/SQL Packages and Types Reference.
Most commonly used technique for predicting a specific outcome such as response / no-response, high / medium / low-value customer, likely to buy / not buy.
Generalized Linear Models Logistic Regression—classic statistical technique available inside the Oracle Database in a highly performant, scalable, parallized implementation (applies to all OAA ML algorithms). Supports text and transactional data (applies to nearly all OAA ML algorithms)
Naive Bayes—Fast, simple, commonly applicable. Leverages Database's speed in counting.
Technique for predicting a continuous numerical outcome such as customer lifetime value, house value, process yield rates.
Generalized Linear Models Multiple Regression—classic statistical technique but now available inside the Oracle Database as a highly performant, scalable, parallized implementation. Supports ridge regression, feature creation and feature selection. Supports text and transactional data.
Ranks attributes according to strength of relationship with target attribute. Use cases include finding factors most associated with customers who respond to an offer, factors most associated with healthy patients.
Minimum Description Length—Considers each attribute as a simple predictive model of the target class and provides relative influence.
Useful for exploring data and finding natural groupings. Members of a cluster are more like each other than they are like members of a different cluster. Common examples include finding new customer segments, and life sciences discovery.