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

Name: Assessing and Improving Prediction and Classification
Brand: APress
SKU: 9781484233351
Price: 12600 KES
Availability: OutOfStock
ISBN: 9781484233351

1st ed.

by Timothy Masters

Book Details

Format Paperback / softback

ISBN-10 1484233352

ISBN-13 9781484233351

Edition 1st ed.

Publisher APress

Imprint APress

Country of Manufacture US

Country of Publication GB

Publication Date Dec 20th, 2017

Print length 517 Pages

Weight 1,032 grams

Dimensions 18.40 x 26.80 x 3.40 cms

Product Classification: Probability & statistics Probability and statistics Web programming Maths for computer scientists

KSh 12,600.00

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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.

Get Assessing and Improving Prediction and Classification by Timothy Masters at the best price and quality guaranteed only at Werezi Africa's largest book ecommerce store. The book was published by APress and it has 517 pages.