Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/using-oracle-machine-learnin OML4Py Attribute Importance: Use this template notebook to identify key attributes that have maximum influence over the target attribute. The target attribute in the build data of a supervised model is the attribute that you want to predict. The template builds an Attribute Importance model using the SH schema data.
https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpar/ Attribute Importance class oml.ai(model_name=None, model_owner=None, **params) In-database Attribute Importance Model Computes the relative importance of variables (aka attributes or columns) when predicting a target variable (numeric or categorical column). This function exposes the corresponding Oracle Ad-vanced Analytics in-database algorithm. Oracle Advanced Analytics does not support the predic-tion functions for attribute importance. The results of attribute importance are the attributes of the build data ranked according to their predictive influence. The ranking and the measure of im-portance can be used for selecting attributes.
Attributes importance : oml.DataFrame Relative im-portance of predictor variables for predicting a response variable. It includes the following compo-nents: * variable: The name of the predictor variable * importance:
The importance of the predictor variable * rank: The predictor variable rank based on the importance value.

* Oracle Analytics Cloud and Oracle Autonomous Database are two Oracle Cloud services that are integrated with Oracle Machine Learning12.
* Oracle Analytics Cloud is a comprehensive cloud service that provides a unified platform for data preparation, analysis, visualization, and machine learning. It supports Oracle Machine Learning Notebooks, which enable data scientists and other data professionals to work collaboratively using SQL, PL/SQL, Python, and R1.
* Oracle Autonomous Database is a self-driving, self-securing, and self-repairing cloud service that supports various workloads such as data warehousing, transaction processing, and JSON document storage. It includes Oracle Machine Learning Services, which enable users to build, deploy, and manage machine learning models using REST APIs2.

Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/collaborate-oracle-machine-l

Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/use-library-collaborate-users Use Templates to Collaborate with Users In the Oracle Machine Learning Templates UI, you can collaborate with other users by sharing your work, publishing your work as reports, and by creating notebooks from tem-plates. You can store your notebooks as templates, share notebooks, and provide sample templates to other users. Note: You can also collaborate with other Oracle Machine Learning Notebook users by providing access to your workspace. The authenticated user can then access the projects in your workspace, and access your notebooks. The access level depends on the permission type granted - Manager, Developer, or Viewer.
For more information about collaboration among users, see
unre-solvable-reference.html#GUID-7079D42F-9308-4BEF-B2F2-E74E49A22A87 Use the Personal Templates Personal Templates lists the notebook templates that you have created. Use the Shared Templates In the Shared Templates, you can share notebook templates with all authenticated users the notebook templates you create from existing notebooks available in Templates. Use the Example Templates The Example Templates page lists the pre-populated Oracle Machine Learning notebook templates. You can view and use these templates to create your notebooks.

* The step that is not required to be performed by an administrator when adding a new user to Oracle Machine Learning Notebooks is providing the user with Autonomous Database client wallet for remote credentials.
* The client wallet is only needed for remote access to the database using tools such as SQL Developer or Python. For accessing Oracle Machine Learning Notebooks, the user only needs an OML username and password, which are created by the administrator in the Oracle Machine Learning User Management interface