Table of Contents
- Title and Copyright Information
- Preface
- Changes in This Release for Oracle Machine Learning for R
-
1
About Oracle Machine Learning for R
- 1.1 What Is Oracle Machine Learning for R?
- 1.2 Advantages of Oracle Machine Learning for R
- 1.3 Get Online Help for Oracle Machine Learning for R Classes, Functions, and Methods
- 1.4 About Transparently Using R on Oracle Database Data
- 1.5 Oracle Machine Learning for R Global Options
-
2
Get Started with Oracle Machine Learning for R
- 2.1 Use OML4R with Oracle Autonomous Database
-
2.2
Use OML4R with On-Premises Oracle Database
- 2.2.1 About Connecting to the Database
- 2.2.2 Use the ore.connect and ore.disconnect Functions
-
2.2.3
Create and Manage R Objects in Oracle Database
- 2.2.3.1 Using Proxy Objects for Database Data
- 2.2.3.2 Create and Delete Database Tables
- 2.2.3.3 Create Temporary Database Tables
- 2.2.3.4 Create Ordered and Unordered ore.frame Objects
-
2.2.3.5
Save and Manage R Objects in the Database
- 2.2.3.5.1 About Persisting Oracle Machine Learning for R Objects
- 2.2.3.5.2 About OML4R Datastores
- 2.2.3.5.3 Save Objects to a Datastore
- 2.2.3.5.4 Control Access to Datastores
- 2.2.3.5.5 Get Information about Datastore Contents
- 2.2.3.5.6 Restore Objects from a Datastore
- 2.2.3.5.7 Delete a Datastore
- 2.2.3.5.8 About Using a Datastore in Embedded R Execution
- 3 Install third-party packages
-
4
Prepare and Explore Data in the Database
- 4.1 Prepare Data in the Database Using Oracle Machine Learning for R
-
4.2
Explore Data
- 4.2.1 About the Exploratory Data Analysis Functions
- 4.2.2 About the NARROW Data Set for Examples
- 4.2.3 Correlate Data
- 4.2.4 Cross-Tabulate Data
- 4.2.5 Analyze the Frequency of Cross-Tabulations
- 4.2.6 Build Exponential Smoothing Models on Time Series Data
- 4.2.7 Rank Data
- 4.2.8 Sort Data
- 4.2.9 Summarize Data with ore.summary
- 4.2.10 Analyze the Distribution of Numeric Variables
- 4.2.11 Principal Component Analysis
- 4.2.12 Singular Value Decomposition
- 4.3 Data Manipulation Using OREdplyr
- 5 Build Oracle Machine Learning for R Models
-
6
OML4R Classes That Provide Access to In-Database Machine Learning Algorithms
- 6.1 About Building In-Database Models using OML4R
- 6.2 About Model Settings
- 6.3 Shared Settings
- 6.4 Association Rules
- 6.5 Attribute Importance Model
- 6.6 Decision Tree
- 6.7 Expectation Maximization
- 6.8 Explicit Semantic Analysis
- 6.9 Exponential Smoothing Model
- 6.10 Extensible R Algorithm Model
- 6.11 Generalized Linear Models
- 6.12 k-Means
- 6.13 Naive Bayes
- 6.14 Neural Network Model
- 6.15 Non-Negative Matrix Factorization
- 6.16 Orthogonal Partitioning Cluster
- 6.17 Partitioned Model
- 6.18 Random Forest Model
- 6.19 Singular Value Decomposition
- 6.20 Support Vector Machine
- 6.21 Text Processing Model
- 6.22 XGBoost Model
- 7 Cross-Validate Models
- 8 Prediction With R Models
-
9
Embedded R Execution
- 9.1 About Embedded R Execution
- 9.2 Datastore and Script Repository Views supporting Embedded R Execution
- 9.3 R Interface for Embedded R Execution
- 9.4 SQL Interface for Embedded R Execution
- 9.5 SQL API for Embedded R Execution with On-premises Database
- 9.6 SQL API for Embedded R Execution with Autonomous Database
- A Oracle Database Views for Oracle Machine Learning for R
- B R Operators and Functions Supported by Oracle Machine Learning for R
- Index