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Kniha Assessing and Improving Prediction and Classification Timothy Masters

Assessing and Improving Prediction and Classification

Autor: Timothy Masters
Jazyk: Angličtina
Vazba: Brožovaná
Vydavatel: APress
Dostupnost: Skladem u dodavatele
Odesíláme za 5-8 dnů
1 788 Kč
Carry out practical, real-life assessments of the performance of prediction and classification model...

Informace o knize

Autor
Timothy Masters
Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2017
Stránek
517
EAN
9781484233351
ISBN
1484233352
Enbook ID
18312544
Vydavatel
APress
Hmotnost
1032
Rozměry
268 x 184 x 34

Kompletní popis

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. What You'll Learn 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 Who This Book Is For Those with at least prior experience with statistics and C++ code.

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