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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Masters, Timothy (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berkeley, CA : Apress : Imprint: Apress, 2018.
Έκδοση:1st ed. 2018.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.