@sambodhi 2017-04-06T08:30:54.000000Z 字数 8371 阅读 1568

# Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team

机器学习

This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.

KDnuggets recently ran an Automated Data Science and Machine Learning blog contest, which garnered numerous entries and lots of appreciation for the winning posts and a pair of honorable mentions.

KDnuggets最近举办了一个自动数据科学和机器学习博客比赛，获得了众多参赛者的作品提交，涌现了许多获奖作品以及一系列的荣誉称号。

The winning post, titled Contest Winner: Winning the AutoML Challenge with Auto-sklearn, written by Matthias Feurer, Aaron Klein, and Frank Hutten, all of the University of Freiburg, provides an overview of Auto-sklearn, an open-source Python tool that automatically determines effective machine learning pipelines for classification and regression datasets. The project is built around the successful scikit-learn library and won the recent AutoML challenge.

Given the popularity of the post, we asked the authors if they would be interested in answering a few followup questions on themselves, their project, and automated data science in general. What follows is the result of this conversation.

Matthew Mayo: First off, congratulations on winning the KDnuggets Automated Data Science and Machine Learning Blog Contest, with your entry outlining your project Auto-sklearn. What if we start by having you introduce the members of the team and provide a little information on each of your backgrounds?

Mattew Mayo：首先祝贺你们的Auto-sklearn项目在KDnuggest自动化数据科学和机器学习博客大赛获胜！你们能为读者介绍一下团队成员，并讲述你们每个人的背景情况吗？

Matthias Feurer: I am a 2nd year PhD student in Frank’s group, working on hyperparameter optimization and automated machine learning. Mostly, I’m interested in optimizing pre-defined machine learning pipelines. I started working for Frank during my Master’s studies, being annoyed by hyperparameter tuning in most of my study projects up to that point.

Matthias Feurer：我是Frank集团的二年级博士生，致力于超参数优化和自动化机器学习。大多时间，我的兴趣在优化预定义的机器学习管道。在我硕士研究生期间，就开始为Frank工作，在我的大部分学习项目中，经常为超参数的调整而感到烦恼。

Aaron Klein: I am also a 2nd year PhD student working on automated deep learning. Like Matthias, I was a master student at the University of Freiburg before I joined Frank’s group.

Aaron Klein：我也是Frank集团的二年级博士生，研究方向是超参数优化和自动化机器学习。像Matthias一样，在加入Frank集团之前，我是弗赖堡大学的硕士生。

Frank Hutter: I’m an Assistant Professor in Computer Science at the University of Freiburg, with main interests in artificial intelligence, machine learning, and automated algorithm design. Before moving to Freiburg, I spent nine years at the University of British Columbia in Vancouver, Canada.

Frank Hutter：我是弗莱堡大学计算机科学系的助理教授，主要从事人工智能、机器学习和自动化算法设计。在来到弗赖堡大学之前，我在加拿大温哥华不列颠哥伦比亚大学工作了九年。

All: Besides the three of us (who wrote the blog post for the KDnuggets Blog Contest), the team for our winning submission also consisted of a few more PhD students and postdocs from the University of Freiburg: Katharina Eggensperger, Jost Tobias Springenberg, Hector Mendoza, Manuel Blum, Stefan Falkner, and Marius Lindauer.

The post was very informative and described Auto-sklearn quite well. Is there anything additional of note you would like our readers to know about Auto-sklearn, or any developments that have occurred since this post? Is there anything you can share about future development plans?

One short-term goal is regression, where we can do a lot more. In the long term we would like Auto-sklearn to become a flexible extension of scikit-learn, which helps users optimize their machine learning pipelines. We also want to do lots more work along the lines of Auto-Net, and want to speed up the optimization process dramatically by more reasoning across datasets, across subsets of data, and over time (for anytime algorithms).

To what extent do you think machine learning and data science can be automated, and what degree of human interaction will be required for so-called fully automated systems?

While there are several approaches for tuning the hyperparameters of machine learning pipelines, so far there is only little work on discovering new pipeline building blocks. Auto-sklearn uses a predefined set of preprocessors and classifiers in a fixed order. An efficient way to also come up with new pipelines would be helpful. One can of course continue this line of thinking and try to automate the discovery of new algorithms as done in several recent papers, such as Learning to learn by gradient descent by gradient descent. Humans can also still tune hyperparameters better than automated methods when machine learning models are very expensive to train, such as state-of-the-art deep neural networks for large datasets. We’re working on ways to transform human expert heuristics into fully formalized algorithms; e.g., our Fabolas approach optimizes the hyperparameters of a neural network on small subsets of the data to speed up learning about the best hyperparameters for the full data set.

Considering the previous question, will data scientists be unemployed anytime soon? Or, if too drastic an idea, will the current hype surrounding data scientists be tempered by automation in the near future, and if so, to what degree?

Certainly not. All the methods of automated machine learning are developed to support data scientists, not to replace them. Such methods can free the data scientist from nasty, complicated tasks (like hyperparameter optimization) that can be solved better by machines. But analysing and drawing conclusions still has to be done by human experts -- and in particular data scientists who know the application domain will remain extremely important. We do believe, though, that automation will make individual data scientists a lot more productive, so this might indeed affect the number of data scientists needed to do the job.

What, if anything, can data scientists do to avoid being rendered obsolete? The question, of course, being directed toward adding value as opposed to being mischievous.

It will always take data scientists to analyze and interpret the results of a statistical analysis -- so for young graduates starting data science jobs such skills might be more future-proof than some others (e.g., manual hyperparameter tuning to get the most out of your neural network).

As you have been active in machine learning competitions in the past, do you have any interesting tips, tricks, or insights to share?

Automation and careful resampling strategies. Automation allows to run a lot of experiments, while resampling strategies such as careful cross-validation are needed to prevent against overfitting. It's also often important to go in with an open mind and just let the data speak about which method works best on which dataset.

Finally, where do you think machine learning technology will be in 5 years?

It is hard to predict what will happen in the future, especially in a field that’s changing as quickly as machine learning. E.g., five years ago not many foresaw the rise of deep learning. But we’re fairly confident that machine learning will be used ever more and will be embedded in commercial tools everyone uses.

Thank you for your time. I'm sure that your time is at a premium, and we appreciate you taking a few moments for our readers.

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