Test-driven machine learning : control your machine learning algorithms using test-driven development to achieve quantifiable milestones

Test-driven machine learning control your machine learning algorithms using test-driven development to achieve quantifiable milestones

Control your machine learning algorithms using test-driven development to achieve quantifiable milestones 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, s...

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Detalles Bibliográficos
Otros Autores: Bozonier, Justin, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt Publishing 2015.
Edición:1st edition
Colección:Community experience distilled.
Materias:
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629671806719
Tabla de Contenidos:
  • Cover ; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introducing Test-Driven Machine Learning; Test-driven development; The TDD cycle; Red; Green; Refactor; Behavior-driven development; Our first test; The anatomy of a test; Given; When; Then; TDD applied to machine learning; Dealing with randomness; Different approaches to validating the improved models; Classification overview; Regression; Clustering; Quantifying the classification models; Summary; Chapter 2: Perceptively Testing a Perceptron; Getting started; Summary
  • Chapter 3: Exploring the Unknown with Multi-armed BanditsUnderstanding 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; Regression assumptions; Quantifying model quality; 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 RegressionGenerating logistic data; Measuring model accuracy; Generating a more complex example; Test driving our model; Summary; Chapter 6: You're So Naïve, 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
  • Creating a classifier chooser (it needs to run tests to evaluate classifier performance)Getting choosey; Developing testable documentation; Decision trees; Summary; Chapter 9: Bringing It All Together; Starting at the highest level; The real world; What we've accomplished; Summary; Index

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