What is the process of identifying similar cases and applying insights to new records called?

The process of identifying similar cases and applying insights to new records is known as Memory-Based Reasoning. This approach involves using historical data to assess new observations by finding cases with features similar to the new records. When a new instance is encountered, the model looks for instances in the dataset that share characteristics with the new case, thus leveraging past information to make predictions or decisions.

This method is particularly effective for tasks such as recommendation systems, where the characteristics of previously observed cases inform the decisions made about new cases. Memory-Based Reasoning essentially captures the essence of a flexible, case-based approach, prioritizing similarity between past and present instances to infer outcomes or categorization.

In contrast, while data mining refers to the broader practice of discovering patterns and insights in large datasets, and classification analysis focuses on assigning categories to observations based on their features, these do not specifically emphasize the direct comparison of new records with similar historical cases. Regression analysis, on the other hand, is focused on predicting a continuous outcome variable based on one or more predictor variables, rather than finding similar cases. Thus, Memory-Based Reasoning highlights the crucial element of similarity in the context of applying insights to new records.