ODM Model View Details Views in Oracle 12.2

    Apr 18, 2017 10:55:00 AM by Brendan Tierney

    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

    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.

    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.

    Tags: Oracle

    Brendan Tierney

    Written by Brendan Tierney

    Brendan Tierney, Oracle ACE Director, is an independent consultant and lectures on Data Mining and Advanced Databases in the Dublin Institute of Technology in Ireland. He has 22+ years of extensive experience working in the areas of Data Mining, Data Warehousing, Data Architecture and Database Design. Brendan has worked on projects in Ireland, UK, Belgium and USA. Brendan is the editor of the UKOUG Oracle Scene magazine and deputy chair of the OUG Ireland BI SIG. Brendan is a regular speaker at conferences across Europe and the USA and has written technical articles for OTN, Oracle Scene, IOUG SELECT Journal and ODTUG Technical Journal. Brendan has published the following books with Oracle Press book Predictive Analytics using Oracle Data Miner Oracle R Enterprise SQL & PL/SQL from the Experts These books are available on Amazon.