Skip to main content
SpringerLink
Log in
Apress

Assessing and Improving Prediction and Classification

Theory and Algorithms in C++

  • Book
  • © 2018

Overview

Authors:
  1. Timothy Masters

    1. Ithaca, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • An expert-driven practical book based on real-life assessment examples of performance and classification models
  • Rich with C++ code examples and analysis of data
  • Contains all you need to know to analyze your C++ prediction and classification algorithms

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (9 chapters)

  1. Front Matter

    Pages i-xx
    Download chapter PDF

  2. Assessment of Numeric Predictions

    • Timothy Masters
    Pages 1-43

  3. Assessment of Class Predictions

    • Timothy Masters
    Pages 45-99

  4. Resampling for Assessing Parameter Estimates

    • Timothy Masters
    Pages 101-184

  5. Resampling for Assessing Prediction and Classification

    • Timothy Masters
    Pages 185-204

  6. Miscellaneous Resampling Techniques

    • Timothy Masters
    Pages 205-278

  7. Combining Numeric Predictions

    • Timothy Masters
    Pages 279-307

  8. Combining Classification Models

    • Timothy Masters
    Pages 309-392

  9. Gating Methods

    • Timothy Masters
    Pages 393-416

  10. Information and Entropy

    • Timothy Masters
    Pages 417-507

  11. Back Matter

    Pages 509-517
    Download chapter PDF
Back to top

Keywords

About this book

Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting.  This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application.


Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models.  This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics.


All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code.  Manyof these techniques are recent developments, still not in widespread use.  Others are standard algorithms given a fresh look.  In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program.




What You'll Learn
  • Compute entropy to detect problematic predictors
  • Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing
  • Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling
  • Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising
  • Use Monte-Carlo permutation methods to assessthe role of good luck in performance results
  • Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions





Who This Book is For


Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book.  Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.


Authors and Affiliations

  • Ithaca, USA

    Timothy Masters

About the author

Timothy Masters received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for flood and drought prediction, detection of hidden missile silos, and identification of threatening military vehicles. Later he worked with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored four books on practical applications of neural networks: Practical Neural Network Recipes in C++ (Academic Press, 1993) Signal and Image Processing with Neural Networks (Wiley, 1994) Advanced Algorithms for Neural Networks (Wiley, 1995) Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995).

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation