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Thoughtful Machine Learning: A Test-Driven Approachby Matthew Kirk
Synopses & Reviews
Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks.
Machine-learning algorithms often have tests baked in, but they cant account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If youre familiar with Ruby 2.1, youre ready to start.
Apply a fully test-driven approach to machine-learning algorithms, and save yourself the pain of missing mistakes in your analyses. Most data scientists have run an analysis and simply accepted any answer that wasnt an error message. But just because it runs doesnt mean its correct. Missed mistakes can ruin research and harm reputations.
All of that can be avoided by writing tests and building checks into your work. This book shows you how to write tests and build checks into their work. Using the Ruby programming language, software developers, business analysts, and CTOs will learn how to test machine-learning code, and understand whats happening "behind the scenes."
About the Author
Matthew Kirk holds a B.S. in Economics and a B.S. in Applied and Computational Mathematical Sciences with a concentration in Quantitative Economics from the University of Washington. He started Modulus 7, a data science and Ruby development consulting firm, in early 2012. Matthew has spoken around the world about using machine learning and data science with Ruby.
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