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Assessing and Improving Prediction and Classification
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eBay-Artikelnr.:397250487573
Artikelmerkmale
- Artikelzustand
- subject_code
- UYQ
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- true
- target_audience
- College/University
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- edition_number
- 1
- binding
- paperback
- edition
- 1st
- MPN
- 49,373,377.00
- batteries_required
- false
- manufacturer
- Apress
- Brand
- Apress
- number_of_items
- 1
- pages
- 540
- genre
- Databases
- part_number
- 49373377
- publication_date
- 2017-12-20T00:00:01Z
- unspsc_code
- 55101500
- batteries_included
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- ISBN
- 9781484233351
Über dieses Produkt
Product Identifiers
Publisher
Apress L. P.
ISBN-10
1484233352
ISBN-13
9781484233351
eBay Product ID (ePID)
239612249
Product Key Features
Number of Pages
Xx, 517 Pages
Language
English
Publication Name
Assessing and Improving Prediction and Classification : Using C++, Algorithms, Data and Statistics
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, Computer Science, Databases / General
Publication Year
2017
Type
Textbook
Subject Area
Mathematics, Computers
Format
Trade Paperback
Dimensions
Item Weight
35.8 Oz
Item Length
10 in
Item Width
7 in
Additional Product Features
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
1. Assessment of Numeric Predictions.- 2. Assessment of Class Predictions.- 3. Resampling for Assessing Parameter Estimates.- 4. Resampling for Assessing Prediction and Classification.- 5. Miscellaneous Resampling Techniques.- 6. Combining Numeric Predictions.- 7. Combining Classification Models.- 8. Gaiting Methods.- 9. Information and Entropy.- References.
Synopsis
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 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 assess the 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., 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.
LC Classification Number
QA76.9.B45
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