Assessing and improving prediction and classification : theory and algorithms in C++ / Timothy Masters.
2018
QA76.73.C153
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Title
Assessing and improving prediction and classification : theory and algorithms in C++ / Timothy Masters.
Author
Masters, Timothy, author.
ISBN
9781484233368 (electronic book)
1484233360 (electronic book)
9781484233351
1484233352
1484233360 (electronic book)
9781484233351
1484233352
Published
New York, NY : Apress, [2018]
Language
English
Description
1 online resource
Item Number
10.1007/978-1-4842-3336-8 doi
Call Number
QA76.73.C153
Dewey Decimal Classification
005.13/3
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
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text file PDF
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Available in Other Form
Print version: 9781484233351
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