Table of Contents
- List of Tables
- Title and Copyright Information
- Preface
- Changes in This Release for Oracle Machine Learning for SQL User's Guide
- Other Changes
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1
Oracle Machine Learning With SQL
- 1.1 Highlights of the Oracle Machine Learning for SQL API
- 1.2 Example: Predicting Likely Candidates for a Sales Promotion
- 1.3 Example: Analyzing Preferred Customers
- 1.4 Example: Segmenting Customer Data
- 1.5 Example : Comparison of Texts Using an ESA Model
- 1.6 Example: Using Vector Data for Dimensionality Reduction and Clustering
- 2 About the Oracle Machine Learning for SQL API
- 3 Prepare the Data
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4
Create a Model
- 4.1 Before Creating a Model
- 4.2 Choose the Machine Learning Technique
- 4.3 Choose the Algorithm
- 4.4 Automatic Data Preparation
- 4.5 Embed Transformations in a Model
- 4.6 The CREATE_MODEL2 Procedure
- 4.7 The CREATE_MODEL Procedure
- 4.8 Specify Model Settings
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4.9
Model Detail Views
- 4.9.1 Model Detail Views for Association Rules
- 4.9.2 Model Detail View for Frequent Itemsets
- 4.9.3 Model Detail Views for Transactional Itemsets
- 4.9.4 Model Detail View for Transactional Rule
- 4.9.5 Model Detail Views for Classification Algorithms
- 4.9.6 Model Detail Views for CUR Matrix Decomposition
- 4.9.7 Model Detail Views for Decision Tree
- 4.9.8 Model Detail Views for Generalized Linear Model
- 4.9.9 Model Detail View for Multivariate State Estimation Technique - Sequential Probability Ratio Test
- 4.9.10 Model Detail Views for Naive Bayes
- 4.9.11 Model Detail Views for Neural Network
- 4.9.12 Model Detail Views for Random Forest
- 4.9.13 Model Detail View for Support Vector Machine
- 4.9.14 Model Detail Views for XGBoost
- 4.9.15 Model Detail Views for Clustering Algorithms
- 4.9.16 Model Detail Views for Expectation Maximization
- 4.9.17 Model Detail Views for k-Means
- 4.9.18 Model Detail Views for O-Cluster
- 4.9.19 Model Detail Views for Explicit Semantic Analysis
- 4.9.20 Model Detail Views for Non-Negative Matrix Factorization
- 4.9.21 Model Detail Views for Singular Value Decomposition
- 4.9.22 Model Detail Views for Minimum Description Length
- 4.9.23 Model Detail Views for Binning
- 4.9.24 Model Detail Views for Global Information
- 4.9.25 Model Detail Views for Normalization and Missing Value Handling
- 4.9.26 Model Detail Views for Exponential Smoothing
- 4.9.27 Model Detail Views for Text Features
- 4.9.28 Model Detail Views for ONNX Models
- 5 Scoring and Deployment
- 6 Machine Learning Operations on Unstructured Text
- 7 Integration of ONNX Runtime
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8
Administrative Tasks for Oracle Machine Learning for SQL
- 8.1 Install and Configure a Database for Oracle Machine Learning for SQL
- 8.2 Upgrade or Downgrade Oracle Machine Learning for SQL
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8.3
Export and Import Oracle Machine Learning for SQL Models
- 8.3.1 About Exporting Models
- 8.3.2 About Oracle Data Pump
- 8.3.3 Options for Exporting and Importing Oracle Machine Learning for SQL Models
- 8.3.4 Directory Objects for EXPORT_MODEL and IMPORT_MODEL
- 8.3.5 Use EXPORT_MODEL and IMPORT_MODEL
- 8.3.6 EXPORT and IMPORT Serialized Models
- 8.3.7 Import From PMML
- 8.4 Secure
- 8.5 Audit and Add Comments to Oracle Machine Learning for SQL Models
- A Oracle Machine Learning for SQL Examples
- Index