Book description
- Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data
- Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data
- Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods
- See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data
- Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high
Product information
- Title: Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications
- Author(s):
- Release date: December 2017
- Publisher(s): Apress
- ISBN: 9781484233153
You might also like
book
Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science
Discover a variety of data-mining algorithms that are useful for selecting small sets of important features …
book
Algorithms in C++ Part 5: Graph Algorithms, Third Edition
Once again, Robert Sedgewick provides a current and comprehensive introduction to important algorithms. The focus this …
book
Algorithms in C++, Parts 1-4: Fundamentals, Data Structure, Sorting, Searching, Third Edition
Robert Sedgewick has thoroughly rewritten and substantially expanded and updated his popular work to provide current …
book
Algorithms in Java, Third Edition, Part 5: Graph Algorithms
Once again, Robert Sedgewick provides a current and comprehensive introduction to important algorithms. The focus this …