7 OML4R Classes That Provide Access to In-Database Machine Learning Algorithms

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.