Book description
- Compute entropy to detect problematic predictors
- Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing
- Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling
- Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising
- Use Monte-Carlo permutation methods to assessthe role of good luck in performance results
- Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions
Table of contents
- Cover
- Front Matter
- 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. Gating Methods
- 9. Information and Entropy
- Back Matter
Product information
- Title: Assessing and Improving Prediction and Classification: Theory and Algorithms in C++
- Author(s):
- Release date: December 2017
- Publisher(s): Apress
- ISBN: 9781484233368
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