Assessing and Improving Prediction and Classification
Language: English
Published by Apress, 2017
- Softcover
- New
Condition: New
£ 62.87
Quantity: Over 20 available
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. An expert-driven practical book based on real-life assessment examples of performance and classification modelsRich with C++ code examples and analysis of dataContains all you need to know to analyze your C++ pr.
Seller Inventory # 175047260
- Title
- Assessing and Improving Prediction and Classification
- 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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About the Author
"About the title" may belong to another edition of this title.
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