Assessing and Improving Prediction and Classification : Theory and Algorithms in C++

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

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 committee...

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Detalles Bibliográficos
Autor principal: Masters, Timothy. author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Berkeley, CA : Apress 2018.
Edición:1st ed. 2018.
Materias:
Big data.
Artificial intelligence.
Mathematical statistics.
Statistics .
Big Data.
Artificial Intelligence.
Probability and Statistics in Computer Science.
Statistics, general.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630450806719
Tabla de Contenidos:
  • 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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