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 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

Table of Contents

Chapter 1. Test-Driven Machine Learning
Chapter 2. A Quick Introduction to Machine Learning
Chapter 3. K-Nearest Neighbors Classification
Chapter 4. Naive Bayesian Classification
Chapter 5. Hidden Markov Models
Chapter 6. Support Vector Machines
Chapter 7. Neural Networks
Chapter 8. Clustering
Chapter 9. Kernel Ridge Regression
Chapter 10. Improving Models and Data Extraction
Chapter 11. Putting It All Together