Assessing and Improving Prediction and Classification

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 withsome 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