ODM Model View Details Views in Oracle 12.2

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A new feature for Oracle Data Mining in Oracle 12.2 is the new Model Details views.

In Oracle 11.2.0.3 and up to Oracle 12.1 you needed to use a range of PL/SQL functions (in DBMS_DATA_MINING package) to inspect the details of a data mining/machine learning model using SQL.

Check out these previous blog posts for some examples of how to use and extract model details in Oracle 12.1 and earlier versions of the database

Association Rules in ODM-Part 3

Extracting the rules from an ODM Decision Tree model

Cluster Details

Viewing Decision Tree Details

Instead of these functions there are now a lot of DB views available to inspect the details of a model. The following table summarises these various DB Views. Check out the DB views I’ve listed after the table, as these views might some some of the ones you might end up using most often.

I’ve now chance of remembering all of these and this table is a quick reference for me to find the DB views I need to use. The naming method used is very confusing but I’m sure in time I’ll get the hang of them.

NOTE: For the DB Views I’ve listed in the following table, you will need to append the name of the ODM model to the view prefix that is listed in the table.

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Data Mining Type Algorithm & Model Details 12.2 DB View Description
Association Association Rules DM$VR generated rules for Association Rules
Frequent Itemsets DM$VI describes the frequent itemsets
Transaction Itemsets DM$VT describes the transactional itemsets view
Transactional Rules DM$VA describes the transactional rule view and transactional itemsets
Classification (General views for Classification models) DM$VT

DM$VC

describes the target distribution for Classification models

describes the scoring cost matrix for Classification models

Decision Tree DM$VP

DM$VI

DM$VO

DM$VM

describes the DT hierarchy & the split info for each level in DT

describes the statistics associated with individual tree nodes

Higher level node description

describes the cost matrix used by the Decision Tree build

Generalized Linear Model DM$VD

DM$VA

describes model info for Linear Regres & Logistic Regres

describes row level info for Linear Regres & Logistic Regres

Naive Bayes DM$VP

DM$VV

describes the priors of the targets for Naïve Bayes

describes the conditional probabilities of Naïve Bayes model

Support Vector Machine DM$VL describes the coefficients of a linear SVM algorithm
Regression ??? Doe 80 50
Clustering (General views for Clustering models) DM$VD

DM$VA

DM$VH

DM$VR

Cluster model description

Cluster attribute statistics

Cluster historgram statistics

Cluster Rule statistics

k-Means DM$VD

DM$VA

DM$VH

DM$VR

k-Means model description

k-Means attribute statistics

k-Means historgram statistics

k-Means Rule statistics

O-Cluster DM$VD

DM$VA

DM$VH

DM$VR

O-Cluster model description

O-Cluster attribute statistics

O-Cluster historgram statistics

O-Cluster Rule statistics

Expectation Minimization DM$VO

DM$VB

DM$VI

DM$VF

DM$VM

DM$VP

describes the EM components

the pairwise Kullback–Leibler divergence

attribute ranking similar to that of Attribute Importance

parameters of multi-valued Bernoulli distributions

mean & variance parameters for attributes by Gaussian distribution

the coefficients used by random projections to map nested columns to a lower dimensional space

Feature Extraction Non-negative Matrix Factorization DM$VE

DM$VI

Encoding (H) of a NNMF model

H inverse matrix for NNMF model

Singular Value Decomposition DM$VE

DM$VV

DM$VU

Associated PCA information for both classes of models

describes the right-singular vectors of SVD model

describes the left-singular vectors of a SVD model

Explicit Semantic Analysis DM$VA

DM$VF

ESA attribute statistics

ESA model features

Feature Section Minimum Description Length DM$VA describes the Attribute Importance as well as the Attribute Importance rank

Normalizing and Error Handling views created by ODM Automatic Data Processing (ADP)

  • DM$VN : Normalization and Missing Value Handling
  • DM$VB : Binning

Global Model Views

  • DM$VG : Model global statistics
  • DM$VS : Computed model settings
  • DM$VW :Alerts issued during model creation

Each one of these new DB views needs their own blog post to explain what informations is being explained in each. I’m sure over time I will get round to most of these.

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