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

• Main Author: Masters, Timothy (Author, http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berkeley, CA : Apress : Imprint: Apress, 2018.
• Edition: 1st ed. 2018.
• Subjects: Big data.; Artificial intelligence.; Mathematical statistics.; Statistics .; Big Data.; Artificial Intelligence.; Probability and Statistics in Computer Science.; Statistics, general.

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...

Table of Contents:

• 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.