Which Node is used to build classification models for targeting rare events?

The Rule Induction Node is particularly designed for building classification models, especially in scenarios where the target events are rare. This is significant because traditional modeling techniques may struggle to accurately predict rare events, leading to poor performance.

The Rule Induction Node uses algorithms that create simple, interpretable rules from the data. These rules classify instances based on conditions derived from the values of input variables. The process is beneficial for rare event classification because it can focus on the characteristics that distinguish the rare class from more prevalent classes, thus improving the model's ability to identify and target these rare events effectively.

In contrast, the Regression Node focuses on predicting continuous outcomes rather than classification, making it less suitable for the task at hand. The Model Comparison Node is utilized for comparing multiple models instead of directly building a model focused on rare events. The Decision Node serves to streamline the data flow in a model-building process but does not itself build models tailored for rare classifications. Thus, the Rule Induction Node is the most appropriate choice for this specific modeling task.