管道(软件)
计算机科学
过度拟合
机器学习
树(集合论)
管道运输
人工智能
多样性(控制论)
特征(语言学)
数据挖掘
工程类
程序设计语言
数学分析
环境工程
哲学
人工神经网络
语言学
数学
作者
Randal S. Olson,Ryan J. Urbanowicz,Peter C. Andrews,Nicole A. Lavender,La Creis R. Kidd,Jason H. Moore
标识
DOI:10.1007/978-3-319-31204-0_9
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI