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Assessing and improving prediction a...

Masters, Timothy.

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Assessing and improving prediction and classification = theory and algorithms in C++ /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Assessing and improving prediction and classification/ by Timothy Masters.
Reminder of title:	theory and algorithms in C++ /
Author:	Masters, Timothy.
Published:	Berkeley, CA :Apress : : 2018.,
Description:	xx, 517 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	C++ (Computer program language) -
Online resource:	http://dx.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. - Berkeley, CA :Apress :2018. - xx, 517 p. :ill., digital ;24 cm.

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:

527229
C++ (Computer program language)

LC Class. No.: QA76.73.C153

Dewey Class. No.: 005.133

Assessing and improving prediction and classification = theory and algorithms in C++ /
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245 1 0 $a Assessing and improving prediction and classification $h [electronic resource] : $b theory and algorithms in C++ / $c by Timothy Masters.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a xx, 517 p. : $b ill., digital ; $c 24 cm.
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.
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650 0 $a Mathematical models. $3 522882
650 0 $a Data mining. $3 562972
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650 2 4 $a Big Data. $3 3134868
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Probability and Statistics in Computer Science. $3 891072
650 2 4 $a Statistics, general. $3 896933
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