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.
