Semantic Scholar Open Access 2016 692 sitasi

TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning

Randal S. Olson J. Moore

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Abstrak

As data science becomes increasingly mainstream, there will be an ever-growing demand for data science tools that are more accessible, flexible, 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 chapter 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 classification accuracy on a supervised classification task. We benchmark TPOT on a series of 150 supervised classification tasks and find that it significantly 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, genetic programming-based AutoML systems show considerable promise in the AutoML domain.

Topik & Kata Kunci

Computer Science

Penulis (2)

Randal S. Olson

J. Moore

Format Sitasi

APA MLA BibTeX

Olson, R.S., Moore, J. (2016). TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning. https://doi.org/10.1007/978-3-030-05318-5_8

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Lihat di Sumber doi.org/10.1007/978-3-030-05318-5_8

Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 692×
Sumber Database: Semantic Scholar
DOI: 10.1007/978-3-030-05318-5_8
Akses: Open Access ✓