What do SAS Enterprise Miner stopping rules help to prevent?

SAS Enterprise Miner stopping rules are primarily designed to prevent overfitting in predictive modeling. Overfitting occurs when a model captures noise in the training data rather than the underlying distribution, leading to poor generalization on unseen data. Stopping rules help determine when to halt the model training process, ensuring that the complexity of the model does not grow unnecessarily beyond what is needed to capture the essential patterns in the data.

Stopping rules in SAS Enterprise Miner monitor various criteria during the model training process, such as improvement in model metrics or validation set performance. By imposing these thresholds, the software helps maintain a balance between model performance and complexity, thereby enhancing model robustness.

The other concepts mentioned do not directly relate to the function of stopping rules. Logworth pertains to the statistical significance of variables and is used in variable selection rather than model training. Missing values are a data quality issue that needs to be addressed separately, while orphan nodes refer to nodes that do not connect to any other nodes in a decision tree, which is not directly related to the stopping rules themselves.