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Record Nr. |
UNINA9910300755503321 |
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Autore |
Masters Timothy |
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Titolo |
Assessing and Improving Prediction and Classification : Theory and Algorithms in C++ / / by Timothy Masters |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (XX, 517 p. 26 illus., 8 illus. in color.) |
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Disciplina |
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Soggetti |
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Big data |
Artificial intelligence |
Mathematical statistics |
Statistics |
Big Data |
Artificial Intelligence |
Probability and Statistics in Computer Science |
Statistics, general |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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