Abstract: The exemplary embodiments may provide a stacked machine learning model ensemble pipeline architecture selector that selects a well-suited stacked machine learning model ensemble pipeline architecture for a specified configuration input and a target data set. The stacked machine learning model ensemble pipeline architecture selector may generate and score possible stacked machine learning model ensemble pipeline architectures to locate one that is well-suited for the target data set and the conforms with the configuration input. The stacked machine learning model ensemble pipeline architecture selector may use genetic programming to generate successive generations of possible stacked ensemble pipeline architectures and to score those architectures to determine how well-suited they are. In this manner, the stacked machine learning model ensemble pipeline architecture selector may converge on an architecture that is well-suited, for example, that meet one or more scores, evaluation metrics, and/or the like.