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Test-Driven Machine Learning / Justin Bozonier
Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Community experience distilledPublisher: Birmingham : Packt Publishing, 2015Publisher: Birmingham, UNKNOWN : Packt PublishingEdition: 1Description: 1 online resource (191)ISBN:- 1784396362
- 9781784396367
- Q325.5
- T55.4-60.8
Contents:
Summary: 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 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 -- 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 Regression -- Generating 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.PPN: PPN: 860191168Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PQE | ZDB-38-EBR
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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