Preface

Consistent, steady improvement is the name of the game in Machine Learning. Sometimes you find yourself implementing an algorithm from scratch; sometimes you're pulling in libraries. You always need the option to try new algorithms and improve performance. Simultaneously, you need to know that performance doesn't degrade.

You could just ask an expert about every change because testing stochastic algorithms seems impossible. That's just as terribly slow as it sounds. What if you could automate checking that your updated algorithms outperform your previous ones? What if you could design your code so that you could swap in an algorithm from another library or pit one that you wrote yourself against what you have? These are all reasons for this book.

We'll be covering what test-driven development is and what value it brings to machine learning. We'll be using nosetests in Python 2.7 to develop our tests. For machine-learning algorithms, we will be using Statsmodels and sci-kit learn. Statsmodels has some great implementations of regression. sci-kit learn is useful for its plethora of supported classification algorithms.