6.1.1 In-Database Algorithms Supported by OML4R

The functions in the OREdm package provide access to the in-database machine learning functionality of Oracle AI Database. You use these functions to build in-database models in the database.

The following table lists the OML4R functions that build in-database models and the corresponding in-database algorithms and functions.

Table 6-1 Oracle Machine Learning for R Model Functions

OML4R Function Name	Algorithm	Machine Learning Technique (Mining Function)
ore.odmAI	Minimum Description Length	Attribute importance for classification or regression
ore.odmAssocRules	Apriori	Association rules
ore.odmDT	Decision Tree	Classification
ore.odmEM	Expectation Maximization	Clustering
ore.odmESA	Explicit Semantic Analysis	Feature extraction
ore.odmGLM	Generalized Linear Models	Classification and regression
ore.odmKMeans	k-Means	Clustering
ore.odmNB	Naive Bayes	Classification
ore.odmNMF	Non-Negative Matrix Factorization	Feature extraction
ore.odmOC	Orthogonal Partitioning Cluster (O-Cluster)	Clustering
ore.odmRAlg	Extensible R Algorithm	Association rules, attribute importance, classification, clustering, feature extraction, and regression
ore.odmSVD	Singular Value Decomposition	Feature extraction
ore.odmSVM	Support Vector Machines	Classification, regression and anomaly detection.
ore.odmNN	Neural Network	Classification and regression
ore.odmRF	Random Forest	Classification
ore.odmXGB	XGBoost	Classification and regression Note: Available only in Oracle Database 21c and later
ore.odmESM	Exponential Smoothing Method	Regression