swMATH ID: 18808
Software Authors: Randal S. Olson, Jason H. Moore
Description: 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.
Homepage: http://www.jmlr.org/proceedings/papers/v64/olson_tpot_2016.pdf
Keywords: automated machine learning, hyperparameter optimization, pipeline optimiza- tion, genetic programming, Pareto optimization, data science, Python
Related Software: Auto-WEKA; auto-sklearn; SMAC; Scikit; Hyperopt; H2O; Python; OpenML; UCI-ml; ML-plan; rapidminer; AutoKeras; GitHub; Spearmint; irace; fastai; Adam; AutoGluon-Tabular; NiaPy; NiaAML
Referenced in: 7 Publications

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