Assessing and Improving Prediction and Classification

Theory and Algorithms in C++

  • Timothy┬áMasters

Table of contents

  1. Front Matter
    Pages i-xx
  2. Timothy Masters
    Pages 1-43
  3. Timothy Masters
    Pages 45-99
  4. Timothy Masters
    Pages 101-184
  5. Timothy Masters
    Pages 205-278
  6. Timothy Masters
    Pages 279-307
  7. Timothy Masters
    Pages 309-392
  8. Timothy Masters
    Pages 393-416
  9. Timothy Masters
    Pages 417-507
  10. Back Matter
    Pages 509-517

About this book


Carry out practical, real-life assessments of the performance of prediction and classification models written in C++. This book discusses techniques for improving the performance of such models by intelligent resampling of training/testing data, combining multiple models into sophisticated committees, and making use of exogenous information to dynamically choose modeling methodologies. Rigorous statistical techniques for computing confidence in predictions and decisions receive extensive treatment. 

Finally, the last part of the book is devoted to the use of information theory in evaluating and selecting useful predictors. Special attention is paid to Schreiber's Information Transfer, a recent generalization of Grainger Causality. Well commented C++ code is given for every algorithm and technique. 

You will:
  • Discover the hidden pitfalls that lurk in the model development process
  • Work with some of the most powerful model enhancement algorithms that have emerged recently
  • Effectively use and incorporate the C++ code in your own data analysis projects
  • Combine classification models to enhance your projects


prediction classification assess improve AI artificial intelligence big data analytics statistics analysis code

Authors and affiliations

  • Timothy┬áMasters
    • 1
  1. 1.IthacaUSA

Bibliographic information