Assessing and Improving Prediction and Classification: Theory and Algorithms in C++

By 作者: Timothy Masters

Pages 页数: 517 pages

Edition 版本: 1st ed.

Language 语言: English

Publisher Finelybook 出版社: Apress

Publication Date 出版日期: 2017-12-20

ISBN-10 书号:1484233352

ISBN-13 书号:9781484233351

**The Book Description robot was collected from Amazon and arranged by Finelybook**

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. Many of 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.

Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions.

Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing.

Improve 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.

Use information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising.

Use Monte-Carlo permutation methods to assess the role of good luck in performance results.

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.

Contents

Chapter 1: Assessment of Numeric Predictions

Chapter 2: Assessment of Class Predictions

Chapter 3: Resampling for Assessing Parameter Estimates

Chapter 4: Resampling for Assessing Prediction and Classification

Chapter 5: Miscellaneous Resampling Techniques

Chapter 6: Combining Numeric Predictions

Chapter 7: Combining Classification Models

Chapter 8: Gating Methods

Chapter 9: Information and Entropy

**下载地址**

Apress Assessing and Improving Prediction and Classification 1484233352.epub

**下载地址**

Apress Assessing and Improving Prediction and Classification 1484233352.pdf

## 最新评论

Honey1天前说：没有下载链接

wojiaoluoxin3天前说：失效了

auroracongwang4天前说：The Algorithm Design Manual 链接失效

yingjg02164天前说：链接失效了

13668920105天前说：压缩文件损坏，请重新上传，谢谢！

12084849961周前 (09-13)说：好的 谢谢

RJ121周前 (09-12)说：谢谢

12084849961周前 (09-12)说：试过了 不好使呢