The target of a supervised model is a special kind of attribute. The target column in the training data contains the historical values used to train the model. The target column in the test data contains the historical values to which the predictions are compared. The act of scoring produces a prediction for the target.
Clustering, feature extraction, association, and anomaly detection models do not use a target.
Nested columns and columns of unstructured data (such as BFILE, CLOB, or BLOB) cannot be used as targets. Target attributes must have a simple data type.
| Mining Function | Target Data Types |
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You can query the *_MINING_MODEL_ATTRIBUTES view to find the target for a given model, as shown in Example 2-2 Example 2-2.