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Test-Driven Machine Learning, by Justin Bozonier
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About This Book
- Build smart extensions to pre-existing features at work that can help maximize their value
- Quantify your models to drive real improvement
- Take your knowledge of basic concepts, such as linear regression and Naive Bayes classification, to the next level and productionalize their models
- Play what-if games with your models and techniques by following the test-driven exploration process
This book is intended for data technologists (scientists, analysts, or developers) with previous machine learning experience who are also comfortable reading code in Python. This book is ideal for those looking for a way to deliver results quickly to enable rapid iteration and improvement.
What You Will Learn- Get started with an introduction to test-driven development and familiarize yourself with how to apply these concepts to machine learning
- Build and test a neural network deterministically, and learn to look for niche cases that cause odd model behaviour
- Learn to use the multi-armed bandit algorithm to make optimal choices in the face of an enormous amount of uncertainty
- Generate complex and simple random data to create a wide variety of test cases that can be codified into tests
- Develop models iteratively, even when using a third-party library
- Quantify model quality to enable collaboration and rapid iteration
- Adopt simpler approaches to common machine learning algorithms
- Use behaviour-driven development principles to articulate test intent
Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences.
The book begins with an introduction to test-driven machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression. You will discover how to test different approaches to Naive Bayes and compare them quantitatively, along with learning how to apply OOP (Object Oriented Programming) and OOP patterns to test-driven code, leveraging scikit-Learn.
Finally, you will walk through the development of an algorithm which maximizes the expected value of profit for a marketing campaign, by combining one of the classifiers covered with the multiple regression example in the book.
- Sales Rank: #1431999 in eBooks
- Published on: 2015-11-27
- Released on: 2015-11-27
- Format: Kindle eBook
About the Author
Justin Bozonier
Justin Bozonier is a data scientist living in Chicago. He is currently a Senior Data Scientist at GrubHub. He has led the development of their custom analytics platform and also led the development of their first real time split test analysis platform which utilized Bayesian Statistics. In addition he has developed machine learning models for data mining as well as for prototyping product enhancements. Justin's software development expertise has earned him acknowledgements in the books Parallel Programming with Microsoft® .NET as well as Flow-Based Programming, Second Edition. He has also taught a workshop at PyData titled Simplified Statistics through Simulation. His previous work experience includes being an Actuarial Systems Developer at Milliman, Inc., contracting as a Software Development Engineer II at Microsoft, and working as a Sr. Data Analyst and Lead Developer at Cheezburger Network amongst other experience.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Completely different approach to ML
By Julian Cook
This is a totally out-of-left-field book on machine learning. Not only is it novel, but I think the ideas will become more important over time, as more models move into production scenarios and need to be iteratively changed and improved, whilst not _breaking_ the model that already works.
The main idea, as you may have guessed, is to change the model development process from exploring data - followed by trying different models, to a more formal approach of wrapping your data in a test framework and then proceeding to develop the model(s).
The initial model (or failing test) can just be a random guesser, after this you continually re-factor your code to improve on the random result or previous iteration of the model (at which point the test passes).
This will seem weird to a statistician, who would insist of emphasizing significance tests or (at least) looking at an ROC curve, which this book also does. The point made by the author is that once you enter the real world of running prediction models in production, you need to move to a process of iterative development, where you have some formal guarantee that the new model is better than the last and that you have not somehow fooled yourself into using something that is not much better than random.
There are a couple of drawbacks to the whole approach though. The first is the pain of setting up the tests in the first place, especially when you have a lot of data. I thought about setting up some synthetic data to test a new model, but it was honestly quicker to step through an example with the real data to figure out whether the code was working or not.
The other issue is with interactive environments, like R and ipython, you can write your code in short sections at the console, then test each section, or model iteration, as you go. This is very efficient and doesn't necessarily work with a 'think up the tests before you do anything' approach.
Even if you don't like the ideas, the book itself is still a very good way to approach statistical model development and should be widely read, especially in organizations where multiple people might touch the ML code.
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