List of Tables
- -1 New Function and Algorithm Settings
- 2-1 Data Dictionary Views for Oracle Machine Learning
- 2-2 Oracle Machine Learning PL/SQL Packages
- 2-3 DBMS_DATA_MINING_TRANSFORM Transformation Methods
- 2-4 OML4SQL Functions
- 2-5 SQL Statistical Functions Supported by OML4SQL
- 3-1 Target Data Types
- 3-2 Grocery Store Data
- 3-3 Missing Value Treatment by Algorithm
- 4-1 Rule View Columns for Transactional Inputs
- 4-2 Rule View for 2-Dimensional Input
- 4-3 Global Name-Value Pairs View for an Association Model
- 4-4 Association Rule Itemsets View
- 4-5 Association Rule Itemsets For Transactional Data View
- 4-6 Association Rules For Transactional Data View
- 4-7 Classification Targets View
- 4-8 Scoring Cost Matrix View
- 4-9 Attribute Importance and Rank View
- 4-10 Row Importance and Rank View
- 4-11 CUR Matrix Decomposition Statistics Information In Model Global View.
- 4-12 Decision Tree Hierarchy View
- 4-13 Decision Tree Statistics View
- 4-14 Decision Tree Nodes View
- 4-15 Decision Tree Build Cost Matrix View
- 4-16 Global Name-Value Pairs View
- 4-17 Model View for Linear and Logistic Regression Models
- 4-18 GLM Regression Row Diagnostics View for Linear Regression
- 4-19 GLM Regression Row Diagnostics View for Logistic Regression
- 4-20 Global Details for Linear Regression
- 4-21 Global Details for Logistic Regression
- 4-22 MSET-SPRT Information in the Model Global View
- 4-23 Naive Bayes Target Priors View for Naive Bayes
- 4-24 Naive Bayes Conditional Probabilities View for Naive Bayes
- 4-25 Global Name-Value Pairs View for Naive Bayes
- 4-26 Neural Network Weights View
- 4-27 Global Name-Value Pairs Viewfor Neural Network
- 4-28 Variable Importance Model View
- 4-29 Random Forest Statistics Information In Model Global View
- 4-30 Linear Coefficient View for Support Vector Machine
- 4-31 Support Vector Statistics Information In Model Global View
- 4-32 Feature Importance View for a Tree Model
- 4-33 Feature Importance View for a Linear Model
- 4-34 Clustering Description View
- 4-35 Clustering Attribute Statistics
- 4-36 Clustering Histograms View
- 4-37 Clustering Rules View
- 4-38 Expectation Maximization Components View
- 4-39 Expectation Maximization Bernoulli parameters View
- 4-40 Unsupervised Attribute Importance View for Expectation Maximization
- 4-41 Attribute Pair Kullback-Leibler Divergence View for Expectation Maximization
- 4-42 Projection table for Expectation Maximization
- 4-43 Global Details for Expectation Maximization
- 4-44 Clustering Description for k-Means
- 4-45 k-Means Scoring Centroids View
- 4-46 k–Means Global Name-Value Pairs View
- 4-47 Cluster Description View for O-Cluster
- 4-48 Clustering Histograms View for O-Cluster
- 4-49 O-Cluster Statistics Information In Model Global View
- 4-50 Explicit Semantic Analysis Matrix for Feature Extraction
- 4-51 Explicit Semantic Analysis Matrix for Classification
- 4-52 Explicit Semantic Analysis Features for Explicit Semantic Analysis
- 4-53 Explicit Semantic Analysis Statistics Information In Model Global View
- 4-54 Non-Negative Matrix Factorization H Matrix View
- 4-55 Non-Negative Matrix Factorization Inverse H Matrix View
- 4-56 Global Name-Value Pairs View for NMF
- 4-57 Singular Value Decomposition S Matrix View
- 4-58 Singular Value Decomposition V Matrix View
- 4-59 Singular Value Decomposition U Matrix View or Projection Data in Principal Components
- 4-60 Global Name-Value Pairs View for Singular Value Decomposition
- 4-61 Attribute Importance View for Minimum Description Length
- 4-62 Global Name-Value Pairs View for MDL
- 4-63 Model Details View for Binning
- 4-64 Global Name-Value Pairs View
- 4-65 Model Build Alerts View
- 4-66 Computed Settings View
- 4-67 Normalization and Missing Value Handling View
- 4-68 Global Name-Value Pairs View for ESM
- 4-69 Text Feature View for Extracted Text Features
- 4-70 Preparation for Creating an Oracle Machine Learning for SQL Model
- 4-71 Oracle Machine Learning mining_function Values
- 4-72 Oracle Machine Learning Algorithms
- 4-73 Oracle Machine Learning Algorithms With ADP
- 4-74 Fields in a Transformation Record for an Attribute
- 4-75 Binning Methods in DBMS_DATA_MINING_TRANSFORM
- 4-76 Normalization Methods in DBMS_DATA_MINING_TRANSFORM
- 4-77 Outlier Treatment Methods in DBMS_DATA_MINING_TRANSFORM
- 4-78 Settings Table Required Columns
- 4-79 General Model Settings
- 4-80 Algorithm-Specific Model Settings
- 4-81 Cost Matrix Table Required Columns
- 4-82 Priors Table Required Columns
- 4-83 Class Weights Table Required Columns
- 4-84 ALL_MINING_MODEL_SETTINGS
- 5-1 Sample Cost Matrix
- 5-2 APPLY Output Table
- 6-1 Column Data Types That May Contain Unstructured Text
- 6-2 Model Settings for Text
- 6-3 CTX_DDL.CREATE_POLICY Procedure Parameters
- 6-4 Attribute-Specific Text Transformation Instructions
- 7-1 Export and Import Options for Oracle Machine Learning for SQL
- 7-2 System Privileges Granted by dmshgrants.sql to the OML4SQL User
- 7-3 System Privileges for Oracle Machine Learning for SQL
- 7-4 Object Privileges for Oracle Machine Learning for SQL Models
- A-1 Models Created by Examples
- A-2 Views Created by dmsh.sql