4.2 Build Oracle Machine Learning for SQL Models

Use the functions in the OREdm package of Oracle Machine Learning for R to build Oracle Machine Learning for SQL models in R.

These functions are described in the following topics:

• About Building OML4SQL Models using OML4R
  Oracle Machine Learning for SQL functions can process tables, views, star schemas, transactional data, and unstructured data.
• Association Rules
  The ore.odmAssocRules function implements the Apriori algorithm to find frequent itemsets and generate an association model.
• Attribute Importance Model
  The ore.odmAI attribute important function ranks attributes according to their significance in predicting a target.
• Decision Tree
  The ore.odmDT function uses the in-database Decision Tree algorithm, which is based on conditional probabilities.
• Expectation Maximization
  The ore.odmEM function creates a model that uses the in-database Expectation Maximization (EM) algorithm.
• Explicit Semantic Analysis
  The ore.odmESA function creates a model that uses the in-database Explicit Semantic Analysis (ESA) algorithm.
• Extensible R Algorithm Model
  The ore.odmRAlg function creates an Extensible R algorithm model.
• Generalized Linear Models
  The ore.odmGLM function builds a Generalized Linear Model (GLM) model, which includes and extends the class of linear models (linear regression).
• k-Means
  The ore.odmKM function uses the in-database k-Means (KM) algorithm, a distance-based clustering algorithm that partitions data into a specified number of clusters.
• Naive Bayes
  The ore.odmNB function builds an in-database Naive Bayes model.
• Non-Negative Matrix Factorization
  The ore.odmNMF function builds an in-database Non-Negative Matrix Factorization (NMF) model for feature extraction.
• Orthogonal Partitioning Cluster
  The ore.odmOC function builds an in-database model using the Orthogonal Partitioning Cluster (O-Cluster) algorithm.
• Singular Value Decomposition
  The ore.odmSVD function creates a model that uses the in-database Singular Value Decomposition (SVD) algorithm.
• Support Vector Machine
  The ore.odmSVM function builds an OML4R Support Vector Machine (SVM) model.
• Build a Partitioned Model
  A partitioned model is an ensemble model that consists of multiple sub-models, one for each partition of the data.
• Text Processing Model
  A text processing model uses ctx.settings arguments to specify Oracle Text attribute settings.