Assessing and Improving Prediction and Classification, 1st ed. Theory and Algorithms in C++
Auteur : Masters Timothy
- 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 assess the role of good luck in performance results
- Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions
An expert-driven practical book based on real-life assessment examples of performance and classification models
Rich with C++ code examples and analysis of data
Contains all you need to know to analyze your C++ prediction and classification algorithms
Date de parution : 12-2017
Ouvrage de 517 p.
17.8x25.4 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 79,11 €
Ajouter au panierThèmes d’Assessing and Improving Prediction and Classification :
Mots-clés :
prediction; classification; assess; improve; AI; artificial; intelligence; big data; analytics; statistics; analysis; code