Table of Contents
- Title and Copyright Information
- Preface
- 1 What's New in Oracle Machine Learning for R
-
2
About Oracle Machine Learning for R
- 2.1 What Is Oracle Machine Learning for R?
- 2.2 Advantages of Oracle Machine Learning for R
- 2.3 Get Online Help for Oracle Machine Learning for R Classes, Functions, and Methods
- 2.4 About Transparently Using R on Oracle Database Data
- 2.5 Oracle Machine Learning for R Global Options
-
3
Get Started with Oracle Machine Learning for R
- 3.1 Use OML4R with Oracle Autonomous Database
-
3.2
Use OML4R with an On-Premises Oracle Database
- 3.2.1 About Connecting to the Database
- 3.2.2 Use the ore.connect and ore.disconnect Functions
-
3.2.3
Create and Manage R Objects in Oracle Database
- 3.2.3.1 Using Proxy Objects for Database Data
- 3.2.3.2 Create and Delete Database Tables
- 3.2.3.3 Create Temporary Database Tables
- 3.2.3.4 Create Ordered and Unordered ore.frame Objects
-
3.2.3.5
Save and Manage R Objects in the Database
- 3.2.3.5.1 About Persisting Oracle Machine Learning for R Objects
- 3.2.3.5.2 About OML4R Datastores
- 3.2.3.5.3 Save Objects to a Datastore
- 3.2.3.5.4 Control Access to Datastores
- 3.2.3.5.5 Get Information about Datastore Contents
- 3.2.3.5.6 Restore Objects from a Datastore
- 3.2.3.5.7 Delete a Datastore
- 3.2.3.5.8 About Using a Datastore in Embedded R Execution
- 4 Install third-party packages
-
5
Prepare and Explore Data in the Database
- 5.1 Prepare Data in the Database Using Oracle Machine Learning for R
-
5.2
Explore Data
- 5.2.1 About the Exploratory Data Analysis Functions
- 5.2.2 About the NARROW Data Set for Examples
- 5.2.3 Correlate Data
- 5.2.4 Cross-Tabulate Data
- 5.2.5 Analyze the Frequency of Cross-Tabulations
- 5.2.6 Build Exponential Smoothing Models on Time Series Data
- 5.2.7 Rank Data
- 5.2.8 Sort Data
- 5.2.9 Summarize Data with ore.summary
- 5.2.10 Analyze the Distribution of Numeric Variables
- 5.2.11 Principal Component Analysis
- 5.2.12 Singular Value Decomposition
- 5.3 Data Manipulation Using OREdplyr
- 6 Build Oracle Machine Learning for R Models
-
7
OML4R Classes That Provide Access to In-Database Machine Learning Algorithms
- 7.1 About Building In-Database Models using OML4R
- 7.2 About Model Settings
- 7.3 Shared Settings
- 7.4 Association Rules
- 7.5 Attribute Importance Model
- 7.6 Decision Tree
- 7.7 Expectation Maximization
- 7.8 Explicit Semantic Analysis
- 7.9 Exponential Smoothing Model
- 7.10 Extensible R Algorithm Model
- 7.11 Generalized Linear Models
- 7.12 k-Means
- 7.13 Naive Bayes
- 7.14 Neural Network Model
- 7.15 Non-Negative Matrix Factorization
- 7.16 Orthogonal Partitioning Cluster
- 7.17 Partitioned Model
- 7.18 Random Forest Model
- 7.19 Singular Value Decomposition
- 7.20 Support Vector Machine
- 7.21 Text Processing Model
- 7.22 XGBoost Model
- 8 Cross-Validate Models
- 9 Prediction With R Models
-
10
Embedded R Execution
- 10.1 About Embedded R Execution
- 10.2 Datastore and Script Repository Views supporting Embedded R Execution
- 10.3 R Interface for Embedded R Execution
- 10.4 SQL Interface for Embedded R Execution
- 10.5 SQL API for Embedded R Execution with On-premises Database
-
10.6
SQL API for Embedded R Execution with Autonomous Database
- 10.6.1 Access and Authorization Procedures and Functions
-
10.6.2
Embedded R Execution Functions (Autonomous Database)
- 10.6.2.1 rqGrant Function
- 10.6.2.2 rqRevoke Procedure
- 10.6.2.3 rqListEnvs Function
- 10.6.2.4 rqEval2 Function
- 10.6.2.5 rqTableEval2 Function
- 10.6.2.6 rqRowEval2 Function
- 10.6.2.7 rqGroupEval2 Function
- 10.6.2.8 rqIndexEval2 Function
- 10.6.2.9 sys.rqScriptCreate Procedure
- 10.6.2.10 sys.rqScriptDrop Procedure
- 10.6.3 Asynchronous Jobs
- 10.6.4 Special Control Arguments
- 10.6.5 Output Formats
- A Oracle Database Views for Oracle Machine Learning for R
- B R Operators and Functions Supported by Oracle Machine Learning for R
- Index