Automating biomedical data science through tree-based pipeline optimization
arXiv:1601.07925
Abstract
Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators---such as synthetic feature constructors---that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.
16 pages, 5 figures, to appear in EvoBIO 2016 proceedings
References in corpus (1)
Cited by in corpus (8)
- Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
- Autostacker: A Compositional Evolutionary Learning System
- Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording
- A Very Brief and Critical Discussion on AutoML
- Muddling Labels for Regularization, a novel approach to generalization
- Can Evolutionary Sampling Improve Bagged Ensembles?
- Optimizing generalization on the train set: a novel gradient-based framework to train parameters and hyperparameters simultaneously
- An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)