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Assessing and Improving Prediction and Classification = Theory and Algorithms in C++ /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Assessing and Improving Prediction and Classification/ by Timothy Masters.
Reminder of title:	Theory and Algorithms in C++ /
Author:	Masters, Timothy.
Description:	XX, 517 p. 26 illus., 8 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/978-1-4842-3336-8
ISBN:	9781484233368

Assessing and Improving Prediction and Classification = Theory and Algorithms in C++ /
Masters, Timothy.

Assessing and Improving Prediction and ClassificationTheory and Algorithms in C++ /[electronic resource] :by Timothy Masters. - 1st ed. 2018. - XX, 517 p. 26 illus., 8 illus. in color.online resource.

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.

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.

ISBN: 9781484233368

Standard No.: 10.1007/978-1-4842-3336-8doiSubjects--Topical Terms:

981821
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Assessing and Improving Prediction and Classification = Theory and Algorithms in C++ /
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