Classifier Development in R

This book is intended to serve as an introduction to production-level classifier development in the R programming language. The sections denoted by * are optional.

• Introduction

  • What is a production-level classifier?
  • Setting up your development environment
  • A review of R
  • Exercises
  • * Troubleshooting

• Data Preparation

  • Training versus prediction
  • Some manual exercises
    • Filtering out values
  • Transformations
    • Column transformations
    • Row transformations
    • Multi-column transformations
    • Dataframe transformations
    • Exercises
  • Mungebits
    • A simple filter
    • A simple imputer
    • The mungebit data structure
    • The imputer mungebit
    • More advanced mungebits
    • Passing state between training and prediction
    • Exercises
    • *Writing our own mungebit
    • *Testing mungebits
  • Mungepieces
    • An imputer with memory
    • Differences between training and prediction
    • Using the parse_mungepiece function to make mungepieces
    • Exercises
  • Munging
    • Putting it all together with munge
    • Re-munging against a data.frame
    • *Inspecting stored mungepieces
    • *Debugging the data preparation process

• Modeling

  • Some manual exercises
    • A linear regression
    • A GBM model
  • Tundra (docs/turning models into R objects)
    • A linear regression tundra model
    • A GBM tundra model
    • Understanding training parameters
    • Using prediction parameters
    • The philosophy of tundra
    • * Writing our own GLM tundra container
  • Stagerunner
    • An example of the full modeling process
    • Introducing stagerunner: parametrizing our modeling process
    • Quick detour: other use cases for stagerunner
    • * Advanced features of stagerunner objects
    • * Debugging stagerunner objects
    • Interactive stagerunners and caching
    • An ensemble of stagerunners
  • Syberia
    • Introduction
    • The import stage
    • The data stage
    • The model stage
    • The export stage
    • * The evaluation stage
    • Testing your syberia models
    • Re-factoring complicated models with Ramd

• Deployment

  • Microserver
    • Launching a microserver on EC2
    • Deploying your syberia model to S3
  • Validation
    • The philosophy of validation
    • Testing alpha versus beta operations
  • Scaling with nginx