國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Assessing and Improving Prediction and Classification/ by Timothy Masters.
其他題名:	Theory and Algorithms in C++ /
作者:	Masters, Timothy.
面頁冊數:	XX, 517 p. 26 illus., 8 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Big data. -
電子資源:	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++ /
LDR:02872nam a22003975i 4500 001 994221
003 DE-He213
005 20200703133616.0
007 cr nn 008mamaa
008 201225s2018 xxu| s |||| 0|eng d
020 $a 9781484233368 $9 978-1-4842-3336-8
024 7 $a 10.1007/978-1-4842-3336-8 $2 doi
035 $a 978-1-4842-3336-8
050 4 $a QA76.9.B45
072 7 $a UN $2 bicssc
072 7 $a COM021000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 005.7 $2 23
100 1 $a Masters, Timothy. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1204136
245 1 0 $a Assessing and Improving Prediction and Classification $h [electronic resource] : $b Theory and Algorithms in C++ / $c by Timothy Masters.
250 $a 1st ed. 2018.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a XX, 517 p. 26 illus., 8 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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.
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.
650 0 $a Big data. $3 981821
650 0 $a Artificial intelligence. $3 559380
650 0 $a Mathematical statistics. $3 527941
650 0 $a Statistics . $3 1253516
650 1 4 $a Big Data. $3 1017136
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Statistics, general. $3 671463
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484233351
776 0 8 $i Printed edition: $z 9781484233375
776 0 8 $i Printed edition: $z 9781484247129
856 4 0 $u https://doi.org/10.1007/978-1-4842-3336-8
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)