Create and Train a Predictive Model

Advanced data analysts create aand train predictive models so that they can use them to deploy Oracle Machine Learning algorithms to mine datasets, predict a target value, or identify classes of records. Use the data flow editor to create and train predictive models and apply them to your data.

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Arriving at an accurate model is an iterative process and an advanced data analyst can try different models, compare their results, and fine tune parameters based on trial and error. A data analyst can use the finalized, accurate predictive model to predict trends in other datasets, or add the model to workbooks.

Note:

If you're using data sourced from Oracle Autonomous AI Lakehouse, you can use the AutoML capability to quickly and easily train a predictive model for you, without requiring machine learning skills. See Train a Predictive Model Using AutoML in Autonomous Data Warehouse.

Oracle Analytics provides algorithms for numeric prediction, multi-classification, binary-classification and clustering.

The algorithms aren't available until you install Oracle machine learning into your local Oracle Analytics Desktop directory. See How do I install Machine Learning for Desktop?

  1. On your home page, click Create, and then select Data Flow.
  2. Select the dataset that you want to use to train the model. Click Add.
  3. In the data flow editor, click Add a step (+).
    After adding a dataset, you can either use all columns in the dataset to build the model or select only the relevant columns. Choosing the relevant columns requires an understanding of the dataset. Ignore columns that you know won't influence the outcome behavior or that contain redundant information. You can choose only relevant columns by adding the Select Columns step. If you're not sure about the relevant columns, then use all columns.
  4. Select one of the train model steps (for example, Train Numeric Prediction, or Train Clustering).
  5. Select an algorithm and click OK.
  6. If you're working with a supervised model like prediction or classification, then click Target and select the column that you're trying to predict. For example, if you're creating a model to predict a person's income, then select the Income column.
    If you're working with an unsupervised model like clustering, then no target column is required.
  7. Change the default settings for your model to fine tune and improve the accuracy of the predicted outcome. The model you're working with determines these settings.
  8. Click the Save Model step and provide a name and description.
  9. Click Save, enter a name and description of the data flow, and click OK to save the data flow.
  10. Click Run Data Flow to create the predictive model based on the input dataset and model settings that you provided.