Assessing and Improving Prediction and Classification: Theory and Algorithms in C++ (program disk)(sample code only)

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. 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. 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. Carry out practical, real-life assessments of the performance of prediction and classification models written in C++. This book discusses techniques for improving the performance of such models by intelligent resampling of training/testing data, combining multiple models into sophisticated committees, and making use of exogenous information to dynamically choose modeling methodologies. Rigorous statistical techniques for computing confidence in predictions and decisions receive extensive treatment. Finally, the last part of the book is devoted to the use of information theory in evaluating and selecting useful predictors. Special attention is paid to Schreiber's Information Transfer, a recent generalization of Grainger Causality. Well commented C++ code is given for every algorithm and technique. You will: Discover the hidden pitfalls that lurk in the model development process Work with some of the most powerful model enhancement algorithms that have emerged recently Effectively use and incorporate the C++ code in your own data analysis projects Combine classification models to enhance your projects Carry out practical, real-life assessments of the performance of prediction and classification models written in C++. This book discusses techniques for improving the performance of such models by intelligent resampling of training/testing data, combining multiple models into sophisticated committees, and making use of exogenous information to dynamically choose modeling methodologies. Rigorous statistical techniques for computing confidence in predictions and decisions receive extensive treatment.Ʃnally, the last part of the book is devoted to the use of information theory in evaluating and selecting useful predictors. Special attention is paid to Schreiber's Information Transfer, a recent generalization of Grainger Causality. Well commented C++ code is given for every algorithm and technique.ٯu will: Discover the hidden pitfalls that lurk in the model development process Work with some of the most powerful model enhancement algorithms that have emerged recently Effectively use and incorporate the C++ code in your own data analysis projects Combine classification models to enhance your projects Front Matter ....Pages i-xx Assessment of Numeric Predictions (Timothy Masters)....Pages 1-43 Assessment of Class Predictions (Timothy Masters)....Pages 45-99 Resampling for Assessing Parameter Estimates (Timothy Masters)....Pages 101-184 Resampling for Assessing Prediction and Classification (Timothy Masters)....Pages 185-204 Miscellaneous Resampling Techniques (Timothy Masters)....Pages 205-278 Combining Numeric Predictions (Timothy Masters)....Pages 279-307 Combining Classification Models (Timothy Masters)....Pages 309-392 Gating Methods (Timothy Masters)....Pages 393-416 Information and Entropy (Timothy Masters)....Pages 417-507 Back Matter ....Pages 509-517