TPOT

TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning. As data science becomes more mainstream, there will be an ever-growing demand for data science tools that are more accessible, exible, and scalable. In response to this demand, automated machine learning (AutoML) researchers have begun building systems that automate the process of designing and optimizing machine learning pipelines. In this paper we present TPOT v0.3, an open source genetic programming-based AutoML system that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classi cation accuracy on a supervised classi cation task. We benchmark TPOT on a series of 150 supervised classi cation tasks and nd that it signi cantly outperforms a basic machine learning analysis in 21 of them, while experiencing minimal degradation in accuracy on 4 of the benchmarks|all without any domain knowledge nor human input. As such, GP-based AutoML systems show considerable promise in the AutoML domain.


References in zbMATH (referenced in 11 articles )

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  1. Bakirov, Rashid; Fay, Damien; Gabrys, Bogdan: Automated adaptation strategies for stream learning (2021)
  2. Luka Pečnik; Iztok Fister Jr.: NiaAML: AutoML framework based on stochastic population-based nature-inspired algorithms (2021) not zbMATH
  3. Škrlj, Blaž; Martinc, Matej; Lavrač, Nada; Pollak, Senja: autoBOT: evolving neuro-symbolic representations for explainable low resource text classification (2021)
  4. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  5. Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander Smola: AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (2020) arXiv
  6. Pimenta, Cristiano G.; de Sá, Alex G. C.; Ochoa, Gabriela; Pappa, Gisele L.: Fitness landscape analysis of automated machine learning search spaces (2020)
  7. Rohan Anand, Joeran Beel: Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization (2020) arXiv
  8. Brazdil, Pavel (ed.); Giraud-Carrier, Christophe (ed.): Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue (2018)
  9. Haifeng Jin, Qingquan Song, Xia Hu: Auto-Keras: An Efficient Neural Architecture Search System (2018) arXiv
  10. Mohr, Felix; Wever, Marcel; Hüllermeier, Eyke: ML-plan: automated machine learning via hierarchical planning (2018)
  11. Andrew Sohn, Randal S. Olson, Jason H. Moore: Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming (2017) arXiv