Test-Driven Machine Learning

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测试驱动机器学习 by Justin Bozonier

Contents

  • Chapter 1: Introducing Test-Driven Machine Learning
    • Test-driven development
    • The TDD cycle
    • Behavior-driven development
    • Our first test
    • TDD applied to machine learning
    • Dealing with randomness
    • Different approaches to validating the improved models
    • Quantifying the classification models
    • Summary
  • Chapter 2: Perceptively Testing a Perceptron
    • Getting started
    • Summary
  • Chapter 3: Exploring the Unknown with Multi-armed Bandits
    • Understanding a bandit
    • Testing with simulation
    • Starting from scratch
    • Simulating real world situations
    • A randomized probability matching algorithm
    • A bootstrapping bandit
    • The problem with straight bootstrapping
    • Multi-armed armed bandit throw down
    • Summary
  • Chapter 4: Predicting Values with Regression
    • Refresher on advanced regression
    • Generating our own data
    • Building the foundations of our model
    • Cross-validating our model
    • Generating data
    • Summary
  • Chapter 5: Making Decisions Black and White with Logistic Regression
    • Generating logistic data
    • Measuring model accuracy
    • Generating a more complex example
    • Test driving our model
    • Summary
  • Chapter 6: You’re So Naive, Bayes
    • Gaussian classification by hand
    • Beginning the development
    • Summary
  • Chapter 7: Optimizing by Choosing a New Algorithm
    • Upgrading the classifier
    • Applying our classifier
    • Upgrading to Random Forest
    • Summary
  • Chapter 8: Exploring scikit-learn Test First
    • Test-driven design
    • Planning our journey
    • Getting choosey
    • Developing testable documentation
    • Summary
  • Chapter 9: Bringing It All Together
    • Starting at the highest level
    • The real world
    • What we’ve accomplished
    • Summary
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pip install nose pandas statsmodels scikit-learn