Table of Contents
- Title and Copyright Information
- Preface
-
1
Introduction to Oracle Machine Learning for R
- 1.1 About 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 Typical Operations in Using Oracle Machine Learning for R
- 1.6 Oracle Machine Learning for R Global Options
-
2
Get Started with Oracle Machine Learning for R
- 2.1 Connect to an Oracle Database Instance
-
2.2
Create and Manage R Objects in Oracle Database
- 2.2.1 Create R Objects for In-Database Data
- 2.2.2 Create Ordered and Unordered ore.frame Objects
- 2.2.3 Move Data to and from the Database
- 2.2.4 Create and Delete Database Tables
-
2.2.5
Save and Manage R Objects in the Database
- 2.2.5.1 About Persisting Oracle Machine Learning for R Objects
- 2.2.5.2 About OML4R Datastores
- 2.2.5.3 Save Objects to a Datastore
- 2.2.5.4 Control Access to Datastores
- 2.2.5.5 Get Information about Datastore Contents
- 2.2.5.6 Restore Objects from a Datastore
- 2.2.5.7 Delete a Datastore
- 2.2.5.8 About Using a Datastore in Embedded R Execution
-
3
Prepare and Explore Data in the Database
- 3.1 Prepare Data in the Database Using Oracle Machine Learning for R
-
3.2
Explore Data
- 3.2.1 About the Exploratory Data Analysis Functions
- 3.2.2 About the NARROW Data Set for Examples
- 3.2.3 Correlate Data
- 3.2.4 Cross-Tabulate Data
- 3.2.5 Analyze the Frequency of Cross-Tabulations
- 3.2.6 Build Exponential Smoothing Models on Time Series Data
- 3.2.7 Rank Data
- 3.2.8 Sort Data
- 3.2.9 Summarize Data
- 3.2.10 Analyze the Distribution of Numeric Variables
- 3.2.11 Principal Component Analysis
- 3.2.12 Singular Value Decomposition
- 3.3 Data Manipulation Using OREdplyr
- 3.4 About Using Third-Party Packages on the Client
-
4
Build Models in Oracle Machine Learning for R
- 4.1 Build Oracle Machine Learning for R Models
-
4.2
Build Oracle Machine Learning for SQL Models
- 4.2.1 About Building OML4SQL Models using OML4R
- 4.2.2 Association Rules
- 4.2.3 Attribute Importance Model
- 4.2.4 Decision Tree
- 4.2.5 Expectation Maximization
- 4.2.6 Explicit Semantic Analysis
- 4.2.7 Extensible R Algorithm Model
- 4.2.8 Generalized Linear Models
- 4.2.9 k-Means
- 4.2.10 Naive Bayes
- 4.2.11 Non-Negative Matrix Factorization
- 4.2.12 Orthogonal Partitioning Cluster
- 4.2.13 Singular Value Decomposition
- 4.2.14 Support Vector Machine
- 4.2.15 Build a Partitioned Model
- 4.2.16 Text Processing Model
- 4.3 Cross-Validate Models
- 5 Prediction With R Models
-
6
Use Oracle Machine Learning for R Embedded R Execution
- 6.1 About Oracle Machine Learning for R Embedded R Execution
- 6.2 R Interface for Embedded R Execution
- 6.3 SQL Interface for Embedded R Execution
- A SQL APIs for Oracle Machine Learning for R
- B Oracle Database Views for Oracle Machine Learning for R
- C R Operators and Functions Supported by Oracle Machine Learning for R
- Index