Thoughtful machine learning: a test-driven approach
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 can't account for human errors in coding. Rather than blindly rely o…
Read More ↓Title Statements
- Full title :Thoughtful machine learning : a test-driven approach / Matthew Kirk ; Mike Loukides and Ann Spencer, editors ; Melanie Yarbrough, production editor ; Rachel Monaghan, copyeditor ; Ellie Volkhausen, cover designer.
Summary
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 can't 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 you're familiar with Ruby 2.1, you're ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction.
Read More ↓Edition Statements
- Publication Date:©2015
Publication Statements
- Publisher(s):O'Reilly
- Place of Publication:Sebastopol, California
- Language:eng
- Publication Date:©2015
Notes
Includes index.
The Physical Object
- Description:1 online resource (235 pages) : : illustrations (some color)
Edition Identifiers
- ISBN:9781449374105, 1449374107, 1449374069, 9781449374068
- oclc:ocn893673678
- LC Classification:Q325.5
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