What You'll Learn

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

Who This Book Is For

Anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.