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Assessing and improving prediction and classification : theory and algorithms in C++ /
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 committee...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Apress,
[2018]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Masters, Timothy, |e author. | |
| 245 | 1 | 0 | |a Assessing and improving prediction and classification : |b theory and algorithms in C++ / |c Timothy Masters. |
| 264 | 1 | |a New York, NY : |b Apress, |c [2018] | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a 1. Assessment of Numeric Predictions -- 2. Assessment of Class Predictions -- 3. Resampling for Assessing Parameter Estimates -- 4. Resampling for Assessing Prediction and Classification -- 5. Miscellaneous Resampling Techniques -- 6. Combining Numeric Predictions -- 7. Combining Classification Models -- 8. Gaiting Methods -- 9. Information and Entropy -- References. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a C++ (Computer program language) | |
| 650 | 0 | |a Mathematical models. | |
| 650 | 0 | |a Data mining. | |
| 650 | 2 | |a Models, Theoretical | |
| 650 | 2 | |a Data Mining | |
| 650 | 1 | 4 | |a Computer Science. |
| 650 | 2 | 4 | |a Big Data. |
| 650 | 2 | 4 | |a Artificial Intelligence (incl. Robotics) |
| 650 | 2 | 4 | |a Probability and Statistics in Computer Science. |
| 650 | 2 | 4 | |a Statistics, general. |
| 650 | 6 | |a C++ (Langage de programmation) | |
| 650 | 6 | |a Modèles mathématiques. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 7 | |a mathematical models. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Maths for computer scientists. |2 bicssc | |
| 650 | 7 | |a Probability & statistics. |2 bicssc | |
| 650 | 7 | |a Databases. |2 bicssc | |
| 650 | 7 | |a COMPUTERS |x Programming Languages |x C++ |2 bisacsh | |
| 650 | 7 | |a C++ (Computer program language) |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Mathematical models |2 fast | |
| 776 | 0 | 8 | |i Printed edition: |z 9781484233351 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781484233368/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 880 | |6 520-00/Arab/r |a 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. | ||
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