Use Oracle Machine Learning Models in Oracle Analytics
You can register and use Oracle machine learning models from Oracle Database or Oracle Autonomous Data Warehouse to score data in Oracle Analytics. Use the data flow editor to apply the machine learning models to your data.
Oracle Analytics enables you to build machine-learning into your applications without data scientist expertise.
How Can I Use Oracle Machine Learning Models in Oracle Analytics?
Oracle Analytics allows you to register and use Oracle machine leaning models from Oracle Database or Oracle Autonomous Data Warehouse.
Using Oracle machine learning models with Oracle Analytics greatly increases the level of predictive analytics that you can perform on datasets because the data and the model reside in the database, the data scoring is performed in the database, and the resulting dataset is stored in the database. This allows you to use the Oracle machine learning execution engine to score large datasets.
- Oracle Autonomous Data Warehouse
- Oracle Database
In Oracle Analytics you can register any of the database's Oracle machine learning models in the mining classes Classification, Regression, Clustering, Anomaly, or Feature Extraction that were created using the Oracle Machine Learning for SQL API (OML 4SQL). Your database permissions determine the Oracle machine learning models that are available for you to register and use.
You can also create predictive models in Oracle Analytics.
Register Oracle Machine Learning Models in Oracle Analytics
The Oracle machine learning models must be registered in Oracle Analytics before you can use them to predict data. You can register and use models that reside in your Oracle Database or Oracle Autonomous Data Warehouse data sources.
Inspect Registered Oracle Machine Learning Models
You can access and review information about the Oracle machine learning models that you registered in Oracle Analytics.
View a Registered Model's Details
View an Oracle machine learning model's detail information to help you understand the model and determine if it's suitable for predicting your data. Model details include model class, algorithm, input columns, output columns, and parameters.
- On the Home page, click Navigator, and then click Machine Learning.
- Click the Models tab.
- Hover over the model you want to view, click Actions, and then select Inspect.
- Click Details to view the model's information.
What Are a Registered Model's Views?
When an Oracle machine learning model is created, views containing specific information about the model are generated and stored in the database. Use Oracle Analytics to access a list of a model's views, and then build datasets that you can use to visualize the information contained in the views.
- Scoring Cost Matrix - Describes the scoring matrix for classification models. The view contains
actual_target_value
,predicted_target_value
, andcost
. - Global Name-Value Pairs - Describes global statistics related to the model like number of rows used in the model build and convergence status.
- Decision Tree Statistics - Describes the statistics associated with individual nodes in the decision tree. The statistics include a target histogram for the data in the node. For every node in the tree, this view has information about
predicted_target_value
,actual_target_value
, andnode
support.
Each view's name is unique, for example DM$VCDT_TEST. The format used to generate view names is DM$VAlphabet_Model Name where:
- DM$V - Represents a prefix for views generated from a registered model.
- Alphabet - Represents a value that indicates the type of output model. For example, C indicates that the view type is Scoring Cost Matrix, and G indicated that the view type is Global Name-Value Pair.
- Model Name - Holds the name of the registered Oracle machine learning model and its view. For example, DT_TEST.
For more information about views, see the documentation for your Oracle database version.
Oracle Analytics provides a list of any registered model's views. However, you can only access and visualize views for Oracle Database 12c Release 2 or later. If you're working with an early version of Oracle Database, then you can't use Oracle Analytics to access and visualize views.
View a Registered Model's Views List
A registered model's views are stored in the database, but you can use Oracle Analytics to display a list of the model's views.
Note:
You can access and visualize views for Oracle Database 12c Release 2 or later. If you're working with an earlier version of Oracle Database, then these views don't exist in the database and you can't use Oracle Analytics to access and visualize them.- On the Home page, click Navigator, and then click Machine Learning.
- Click the Models tab.
- Hover over the model you want to view, click Actions, and then select Inspect.
- Click the Related tab to view a list of the model's views.
Visualize a Registered Oracle Machine Learning Model's View
Visualize any of a registered model's views to discover information that helps you better understand and utilize the model.
Note:
You can access and visualize views for Oracle Database 12c Release 2 or later. If you're working with an earlier version of Oracle Database, then these views don't exist in the database and you can't use Oracle Analytics to access and visualize them.- On the Home Page, click Navigator, and then click Machine Learning.
- Locate the registered machine learning model, click Actions, and click Inspect.
- Click Details and confirm that the Model Info section is expanded. Go to the DB Model Owner field and record the database schema name.
- Click Related and locate and record the name of the view. Click Close.
- On the Home page, click Create and click Dataset.
- Select the connection that contains the machine learning model and its views.
- In the Dataset editor, browse for and click the database schema name that you located on the Details tab.
- Select the view that you located on the Related tab, and double-click columns to add them to the dataset. Click Add.
- Click Create Workbook to build visualizations.