About this book
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.
- 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
prediction classification assess improve AI artificial intelligence big data analytics statistics analysis code
- DOI https://doi.org/10.1007/978-1-4842-3336-8
- Copyright Information Timothy Masters 2018
- Publisher Name Apress, Berkeley, CA
- eBook Packages Professional and Applied Computing Professional and Applied Computing (R0)
- Print ISBN 978-1-4842-3335-1
- Online ISBN 978-1-4842-3336-8
- Buy this book on publisher's site