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 ODMPart 3
Extracting the rules from an ODM Decision Tree model
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$VR 
Cluster model description Cluster attribute statistics Cluster historgram statistics Cluster Rule statistics 
kMeans  DM$VD DM$VA DM$VH DM$VR 
kMeans model description kMeans attribute statistics kMeans historgram statistics kMeans Rule statistics 

OCluster  DM$VD DM$VA DM$VH DM$VR 
OCluster model description OCluster attribute statistics OCluster historgram statistics OCluster 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 multivalued 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  Nonnegative 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 rightsingular vectors of SVD model describes the leftsingular 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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