Table of Contents
- Title and Copyright Information
- Preface
- 1 About Oracle Machine Learning for Python
- 2 Install OML4Py Client for Linux for Use With Autonomous Database on Serverless Exadata Infrastructure
-
3
Install OML4Py for On-Premises Databases
- 3.1 OML4Py On Premises System Requirements
- 3.2 Build and Install Python for Linux for On-Premises Databases
- 3.3 Install the Required Supporting Packages for Linux for On-Premises Databases
-
3.4
Install OML4Py
Server for On-Premises Oracle Database
- 3.4.1 Install OML4Py Server for Linux for On-Premises Oracle Database 19c
- 3.4.2 Install OML4Py Server for Linux for On-Premises Oracle Database 21c
- 3.4.3 Verify OML4Py Server Installation for On-Premises Database
- 3.4.4 Grant Users the Required Privileges for On-Premises Database
- 3.4.5 Create New Users for On-Premises Oracle Database
- 3.4.6 Uninstall the OML4Py Server from an On-Premises Database 19c
- 3.5 Install OML4Py Client for On-Premises Databases
- 4 Install OML4Py on Exadata
- 5 Install Third-Party Packages
- 6 Get Started with Oracle Machine Learning for Python
- 7 Prepare and Explore Data
-
8
OML4Py Classes That Provide
Access to In-Database Machine Learning Algorithms
- 8.1 About Machine Learning Classes and Algorithms
- 8.2 About Model Settings
- 8.3 Shared Settings
- 8.4 Export Oracle Machine Learning for Python Models
- 8.5 Automatic Data Preparation
- 8.6 Model Explainability
- 8.7 Attribute Importance
- 8.8 Association Rules
- 8.9 Decision Tree
- 8.10 Expectation Maximization
- 8.11 Explicit Semantic Analysis
- 8.12 Generalized Linear Model
- 8.13 k-Means
- 8.14 Naive Bayes
- 8.15 Neural Network
- 8.16 Random Forest
- 8.17 Singular Value Decomposition
- 8.18 Support Vector Machine
- 9 Automated Machine Learning
-
10
Embedded Python
Execution
- 10.1 About Embedded Python Execution
- 10.2 Parallelism with OML4Py Embedded Python Execution
- 10.3 Embedded Python Execution Views
-
10.4
Python API for Embedded Python
Execution
- 10.4.1 About Embedded Python Execution
- 10.4.2 Run a User-Defined Python Function
- 10.4.3 Run a User-Defined Python Function on the Specified Data
- 10.4.4 Run a Python Function on Data Grouped By Column Values
- 10.4.5 Run a User-Defined Python Function on Sets of Rows
- 10.4.6 Run a User-Defined Python Function Multiple Times
- 10.4.7 Save and Manage User-Defined Python Functions in the Script Repository
-
10.5
SQL API for Embedded Python Execution with
On-premises Database
- 10.5.1 About the SQL API for Embedded Python Execution with On-Premises Database
- 10.5.2 pyqEval Function (On-Premises Database)
- 10.5.3 pyqTableEval Function (On-Premises Database)
- 10.5.4 pyqRowEval Function (On-Premises Database)
- 10.5.5 pyqGroupEval Function (On-Premises Database)
- 10.5.6 pyqGrant Function (On-Premises Database)
- 10.5.7 pyqRevoke Function (On-Premises Database)
- 10.5.8 pyqScriptCreate Procedure (On-Premises Database)
- 10.5.9 pyqScriptDrop Procedure (On-Premises Database)
-
10.6
SQL API for Embedded Python Execution with
Autonomous Database
- 10.6.1 Access and Authorization Procedures and Functions
-
10.6.2
Embedded Python Execution
Functions (Autonomous Database)
- 10.6.2.1 pyqListEnvs Function (Autonomous Database)
- 10.6.2.2 pyqEval Function (Autonomous Database)
- 10.6.2.3 pyqTableEval Function (Autonomous Database)
- 10.6.2.4 pyqRowEval Function (Autonomous Database)
- 10.6.2.5 pyqGroupEval Function (Autonomous Database)
- 10.6.2.6 pyqIndexEval Function (Autonomous Database)
- 10.6.2.7 pyqGrant Function (Autonomous Database)
- 10.6.2.8 pyqRevoke Function (Autonomous Database)
- 10.6.2.9 pyqScriptCreate Procedure (Autonomous Database)
- 10.6.2.10 pyqScriptDrop Procedure (Autonomous Database)
- 10.6.3 Asynchronous Jobs (Autonomous Database)
- 10.6.4 Special Control Arguments (Autonomous Database)
- 10.6.5 Output Formats (Autonomous Database)
- 11 Administrative Tasks for Oracle Machine Learning for Python
- Index