Automated Model Tuning for Retail

Artificial intelligence, especially Machine learning algorithms, plays important roles in building recommendation systems and promotional forecasting systems for retailers. However, training a machine learning model requires the choice of a number of significant features and requires tuning a large set of configurations. Therefore, it takes a long time for humans to find the optimal configuration for one or more predictors. However, the predictive performance of existing automated tuning models is not as good as manually tuning. Besides, the approach cannot be applied to more than one model. This project, will propose a system that can automatically come up with a set of models with corresponding features and configurations for a specific problem (e.g., promotional forecasting) that provides good or acceptable performance for the prediction.

Faculty Supervisor:

Anthony Bonner


Lan Yao


Rubikloud Technologies Inc.


Computer science


Information and communications technologies




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