GENERATING NEW MACHINE LEARNING MODELS BASED ON COMBINATIONS OF HISTORICAL FEATURE-EXTRACTION RULES AND HISTORICAL MACHINE-LEARNING MODELS

摘要

Techniques for generating new machine learning (ML) systems are described. In an example, a computer system receives a request specifying a task and a performance metric for the new ML model via a user interface. In response, the computer system dynamically generates new feature-extraction rules and new machine learning models based on a rule-model combination that would perform the specified task at a level meeting or exceeding the performance metric.