Multi-Objective Automatic Machine Learning with AutoxgboostMC
arXiv:1908.10796
Abstract
AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. They often combine techniques from many different sub-fields of machine learning in order to find a model or set of models that optimize a user-supplied criterion, such as predictive performance. The ultimate goal of such systems is to reduce the amount of time spent on menial tasks, or tasks that can be solved better by algorithms while leaving decisions that require human intelligence to the end-user. In recent years, the importance of other criteria, such as fairness and interpretability, and many others have become more and more apparent. Current AutoML frameworks either do not allow to optimize such secondary criteria or only do so by limiting the system's choice of models and preprocessing steps. We propose to optimize additional criteria defined by the user directly to guide the search towards an optimal machine learning pipeline. In order to demonstrate the need and usefulness of our approach, we provide a simple multi-criteria AutoML system and showcase an exemplary application.
Accepted at Ecmlpkdd Workshop on Automating Data Science 2019
References in corpus (6)
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Practical Bayesian Optimization of Machine Learning Algorithms
- Equality of Opportunity in Supervised Learning
- CatBoost: gradient boosting with categorical features support
- On Fairness and Calibration
- Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
Cited by in corpus (4)
- Towards Green Automated Machine Learning: Status Quo and Future Directions
- Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"
- Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
- Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector