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Books > Computing & IT > Computer programming
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Assessing and Improving Prediction and Classification - Theory and Algorithms in C++ (Paperback, 1st ed.)
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Assessing and Improving Prediction and Classification - Theory and Algorithms in C++ (Paperback, 1st ed.)
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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.
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