OML4R has classes that provide access to in-database. Oracle Machine Learning algorithms. Using in-database Oracle Machine Learning algorithms eliminate data movement and leverages the database for data preparation.

These functions are described in the following topics:

• About Building In-Database Models using OML4R
  The OML4R machine learning interface is built on top of OML4SQL, leveraging the same in-database algorithms, with the ability to use the same algorithm hyperparameters.
• About Model Settings
  You can specify settings that affect the characteristics of a model.
• Shared Settings
  These settings are common to multiple OML4R machine learning classes.
• 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.
• Exponential Smoothing Model
  The ore.odmESM class uses the in-database Exponential Smoothing Model (ESM) algorithm to create a clustering model.
• 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.
• Neural Network Model
  The ore.odmNN class creates a Neural Network (NN) model for classification and regression. The Neural Network models can be used to capture intricate nonlinear relationships between inputs and outputs or to find patterns in data.
• 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.
• Partitioned Model
  A partitioned model is an ensemble model that consists of multiple sub-models, one for each partition of the data.
• Random Forest Model
  The ore.odmRF class creates an in-database Random Forest (RF) model that provides an ensemble learning technique for classification.
• 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.
• Text Processing Model
  A text processing model uses ctx.settings arguments to specify Oracle Text attribute settings.
• XGBoost Model
  The ore.odmXGB class is a scalable gradient tree boosting system that supports both classification and regression. It makes available the open source gradient boosting framework. It prepares training data, calls the in-database XGBoost, builds and persists a model, and applies the model for prediction.