List of Tables

• 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        Preparation for Creating an Oracle Machine Learning for SQL Model
• 4-2        Oracle Machine Learning mining_function Values
• 4-3        Oracle Machine Learning Algorithms
• 4-4        Oracle Machine Learning Algorithms With ADP
• 4-5        Fields in a Transformation Record for an Attribute
• 4-6        Binning Methods in DBMS_DATA_MINING_TRANSFORM
• 4-7        Normalization Methods in DBMS_DATA_MINING_TRANSFORM
• 4-8        Outlier Treatment Methods in DBMS_DATA_MINING_TRANSFORM
• 4-9        Settings Table Required Columns
• 4-10      General Model Settings
• 4-11      Algorithm-Specific Model Settings
• 4-12      Cost Matrix Table Required Columns
• 4-13      Priors Table Required Columns
• 4-14      Class Weights Table Required Columns
• 4-15      ALL_MINING_MODEL_SETTINGS
• 4-16      Rule View Columns for Transactional Inputs
• 4-17      Rule View for 2-Dimensional Input
• 4-18      Global Name-Value Pairs View for an Association Model
• 4-19      Association Rule Itemsets View
• 4-20      Association Rule Itemsets For Transactional Data View
• 4-21      Association Rules For Transactional Data View
• 4-22      Classification Targets View
• 4-23      Scoring Cost Matrix View
• 4-24      Attribute Importance and Rank View
• 4-25      Row Importance and Rank View
• 4-26      CUR Matrix Decomposition Statistics Information In Model Global View.
• 4-27      Decision Tree Hierarchy View
• 4-28      Decision Tree Statistics View
• 4-29      Decision Tree Nodes View
• 4-30      Decision Tree Build Cost Matrix View
• 4-31      Global Name-Value Pairs View
• 4-32      Model View for Linear and Logistic Regression Models
• 4-33      GLM Regression Row Diagnostics View for Linear Regression
• 4-34      GLM Regression Row Diagnostics View for Logistic Regression
• 4-35      Global Details for Linear Regression
• 4-36      Global Details for Logistic Regression
• 4-37      MSET-SPRT Information in the Model Global View
• 4-38      Naive Bayes Target Priors View for Naive Bayes
• 4-39      Naive Bayes Conditional Probabilities View for Naive Bayes
• 4-40      Global Name-Value Pairs View for Naive Bayes
• 4-41      Neural Network Weights View
• 4-42      Global Name-Value Pairs Viewfor Neural Network
• 4-43      Variable Importance Model View
• 4-44      Random Forest Statistics Information In Model Global View
• 4-45      Linear Coefficient View for Support Vector Machine
• 4-46      Support Vector Statistics Information In Model Global View
• 4-47      Feature Importance View for a Tree Model
• 4-48      Feature Importance View for a Linear Model
• 4-49      Clustering Description View
• 4-50      Clustering Attribute Statistics
• 4-51      Clustering Histograms View
• 4-52      Clustering Rules View
• 4-53      Expectation Maximization Components View
• 4-54      Expectation Maximization Bernoulli parameters View
• 4-55      Unsupervised Attribute Importance View for Expectation Maximization
• 4-56      Attribute Pair Kullback-Leibler Divergence View for Expectation Maximization
• 4-57      Projection table for Expectation Maximization
• 4-58      Global Details for Expectation Maximization
• 4-59      Clustering Description for k-Means
• 4-60      k-Means Scoring Centroids View
• 4-61      k–Means Global Name-Value Pairs View
• 4-62      Cluster Description View for O-Cluster
• 4-63      Clustering Histograms View for O-Cluster
• 4-64      O-Cluster Statistics Information In Model Global View
• 4-65      Explicit Semantic Analysis Matrix for Feature Extraction
• 4-66      Explicit Semantic Analysis Matrix for Classification
• 4-67      Explicit Semantic Analysis Features for Explicit Semantic Analysis
• 4-68      Explicit Semantic Analysis Statistics Information In Model Global View
• 4-69      Non-Negative Matrix Factorization H Matrix View
• 4-70      Non-Negative Matrix Factorization Inverse H Matrix View
• 4-71      Global Name-Value Pairs View for NMF
• 4-72      Singular Value Decomposition S Matrix View
• 4-73      Singular Value Decomposition V Matrix View
• 4-74      Singular Value Decomposition U Matrix View or Projection Data in Principal Components
• 4-75      Global Name-Value Pairs View for Singular Value Decomposition
• 4-76      Attribute Importance View for Minimum Description Length
• 4-77      Global Name-Value Pairs View for MDL
• 4-78      Model Details View for Binning
• 4-79      Global Name-Value Pairs View
• 4-80      Model Build Alerts View
• 4-81      Computed Settings View
• 4-82      Normalization and Missing Value Handling View
• 4-83      Exponential Smoothing Forecast View
• 4-84      Global Name-Value Pairs View for ESM
• 4-85      Time Series Regression Build View
• 4-86      Time Series Regression Score View
• 4-87      Text Feature View for Extracted Text Features
• 4-88      
• 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
• 8-1        Export and Import Options for Oracle Machine Learning for SQL
• 8-2        System Privileges Granted by dmshgrants.sql to the OML4SQL User
• 8-3        System Privileges for Oracle Machine Learning for SQL
• 8-4        Object Privileges for Oracle Machine Learning for SQL Models
• A-1        Models Created by Examples
• A-2        Views Created by dmsh.sql