ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning
arXiv:1904.05381
Abstract
Machine learning pipeline potentially consists of several stages of operations like data preprocessing, feature engineering and machine learning model training. Each operation has a set of hyper-parameters, which can become irrelevant for the pipeline when the operation is not selected. This gives rise to a hierarchical conditional hyper-parameter space. To optimize this mixed continuous and discrete conditional hierarchical hyper-parameter space, we propose an efficient pipeline search and configuration algorithm which combines the power of Reinforcement Learning and Bayesian Optimization. Empirical results show that our method performs favorably compared to state of the art methods like Auto-sklearn , TPOT, Tree Parzen Window, and Random Search.
References in corpus (7)
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Cited by in corpus (4)
- SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions
- Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning
- Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
- A Case Study on Optimization of Platooning Coordination