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010   $a1-4842-3336-0
024 7 $a10.1007/978-1-4842-3336-8
035   $a(CKB)4100000001382202
035   $a(DE-He213)978-1-4842-3336-8
035   $a(MiAaPQ)EBC5205531
035   $a(CaSebORM)9781484233368
035   $a(PPN)222231394
035   $a(OCoLC)1020493769
035   $a(OCoLC)on1020493769
035   $a(EXLCZ)994100000001382202
100   $a20171220d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAssessing and Improving Prediction and Classification $eTheory and Algorithms in C++ /$fby Timothy Masters
205   $a1st ed. 2018.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2018.
215   $a1 online resource (XX, 517 p. 26 illus., 8 illus. in color.) 
311   $a1-4842-3335-2 
320   $aIncludes bibliographical references and index.
327   $a1. 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.
330   $aCarry 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.
606   $aBig data
606   $aArtificial intelligence
606   $aMathematical statistics
606   $aStatistics 
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036
606   $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X
615  0$aBig data.
615  0$aArtificial intelligence.
615  0$aMathematical statistics.
615  0$aStatistics .
615 14$aBig Data.
615 24$aArtificial Intelligence.
615 24$aProbability and Statistics in Computer Science.
615 24$aStatistics, general.
676   $a005.133
700   $aMasters$b Timothy$4aut$4http://id.loc.gov/vocabulary/relators/aut$0105163
801  0$bUMI
801  1$bUMI
906   $aBOOK
912   $a9910300755503321
996   $aAssessing and Improving Prediction and Classification$92528352
997   $aUNINA