Assessing and Improving Prediction and Classification: Theory and Algorithms in C++
Language: English
Published by Apress, 2017
- Softcover
- New

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2017. 1st ed. Paperback. . . . . . Books ship from the US and Ireland.
Seller Inventory # V9781484233351
- Title
- Assessing and Improving Prediction and Classification: Theory and Algorithms in C++
- Author
- Timothy Masters
- Publisher
- Apress
- Publication year
- 2017
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1484233352
- ISBN 13
- 9781484233351
- 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
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Kennys Bookstore
Olney, MD, U.S.A.
AbeBooks seller since October 9, 2009
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