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The Decision Tree manages its own data preparation internally. It does not require pre-treatment of the data. The Decision Tree is not affected by Automatic Data Preparation.
The Decision Tree interprets missing values as missing at random. The algorithm does not support nested tables and thus does not support sparse data.
Building Decision Tree model
To build a Decision Tree model, use a Classification Node. In Oracle Data Mining 12c Release 1(12.1) or later, Decision Tree supports nested data. The Decision Tree supports text for Oracle Database 12c, but not for earlier releases.
Testing Decision Tree model
By default, a Classification Node tests all models that it builds. The test data is created by splitting the input data into build and test subsets. You can also test a Decision Tree model using a Test Node.
Tuning Decision Tree model
After you build and test a Decision Tree model, you can tune it.
Applying Decision Tree model
To apply a model, use an Apply Node.