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
1 | pip install nose pandas statsmodels scikit-learn |