This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories,
trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions
of the classic methods, and extensions and novel approaches that have recently been introduced.Along with algorithmic descriptions
of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method
is succinctly featured. R code for implementation of the algorithm is also emphasized.The unique volume provides researchers,
students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.