Before you Begin
This tutorial shows you how to use Oracle Autonomous Data Warehouse AutoML in an Oracle Analytics data flow to build a prediction model without a data science background.
Background
As a business analyst, citizen data scientist, or expert data scientist, you can build and deploy machine learning models in Oracle Analytics using datasets created from Oracle Autonomous Data Warehouse. You don't need a deep understanding of specific algorithms and the modeling process to build and refine usable models.
The AutoML data flow step chooses the appropriate algorithm from the supported algorithms and identifies the features and tuning to use with your data. The selected target value determines whether the Task Type is a classification or regression type. In this example, AutoML chooses Classification for the Task Type because ATTRITION is a Text data type. The Model Ranking Metric indicates to the AutoML process which metric to use in assessing the model quality.
After generating the model from the data flow, you can use it to score your dataset using the Apply Model step in an Oracle Analytics data flow.
What Do You Need?
- Access to Oracle Analytics
- Access to an Oracle Autonomous Data Warehouse dataset with a user that has OML Developer and DWROLE database roles
The Oracle Autonomous Data Warehouse user creating a dataflow using AutoML can't have administrative privileges.
Create a Dataflow
In this section, you create a dataflow using an Oracle Autonomous Data Warehouse dataset, and add the dataflow steps to complete and run the dataflow generating a prediction model.
- Sign in to Oracle Analytics.
- On the Home page, click Create, and then click Dataflow.
- In Add Data, select an Oracle Autonomous Data Warehouse dataset, and click Add.
This example uses an employee attrition dataset for the AutoML example.
- In Data Flow Steps, double-click AutoML. In AutoML, click Select a column next to Target, and then select the column to use from the Available Data list.
In this example, ATTRITION is the target column. AutoML chooses the best algorithm for the Task Type and Model Ranking Metric for your data. The Task Type is Classification and the Model Ranking Metric is Accuracy.
- In the dataflow, click the Save Model node. In Save Model, enter a name in Model name.
In this example, the model name is employee_attrition_model.
- Click Save . In Save Data Flow As, enter a name, and then click OK.
In this example, the dataflow name is emp_attr_df.
- Click Run to generate the prediction model.
- Click Go back .
Review the Prediction Model
In this section, you review Inspect to find details about the generated model and the model's quality.
- On the Home page, click Machine Learning.
- Hover over your model generated by AutoML, click Actions , and then select Inspect.
- Click Quality to view the model accuracy and precision information.
- Click Details to view the input columns, output columns, and parameters used in generating the model.
- Click Related to view the generated datasets.
You can create visualizations with the generated datasets.
- Click More to see the generated metadata.
- Click Close.
Learn More
Create a Prediction Model with AutoML in Oracle Analytics
F77491-02
March 2024
Copyright © 2024, Oracle and/or its affiliates.
Learn how to create a prediction model with an Oracle Autonomous Data Warehouse dataset in Oracle Analytics.
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