Default Behavior for Classification Node

The default behavior for Classification node is described related to the following:

• Algorithms used: For a binary target, the Classification node builds models using the following four algorithms:

  • GLM Classification Models

  • SVM Classification Models

  • Decision Tree Algorithm

  • Naive Bayes

  If the target is not binary, then GLM is not built by default. You can explicitly add a GLM model to the node.
  The models must have the same build data and same target.

  
  

  Note: If do not want to create a particular model, then delete the model from the list of models. The blue check mark to the left side of the model name selects models to be used in subsequent nodes. It does not select models to build.

  
  

• Testing of models: By default, the models are all tested. The test data is created by randomly splitting the build data into a build data set and a test data set. The default ratio for the split is 60:40. That is, 60 percent build and 40 percent test. Oracle Data Miner uses compression when it creates the build and test tables when appropriate.

• Connecting nodes: You can connect both a build Data Source node and a test Data Source node to the Build node.

• Testing models: You can test Classification models using a Test node along with separate test data.

• Interpreting test results

• Tuning models: After testing a classification, you can tune each model.

• Case ID: The case ID is optional. However, if you do not specify a case ID, then the processing will be slower.