Preface
Ensemble learning! This specialized topic of machine learning broadly deals with putting together multiple models with the aim of providing higher accuracy and stable model performance. The ensemble methodology is based on sound theory and its usage has seen successful applications in complex data science scenarios. This book grabs the opportunity of dealing with this important topic.
Moderately sized datasets are used throughout the book. All the concepts—well, most of them—have been illustrated using the software, and R packages have been liberally used to drive home the point. While care has been taken to ensure that all the codes are error free, please feel free to write us with any bugs or errors in the codes. The approach has been mildly validated through two mini-courses based on earlier drafts. The material was well received by my colleagues and that gave me enough confidence to complete the book.
The Packt editorial team has helped a lot with the technical review, and the manuscript reaches you after a lot of refinement. The bugs and shortcomings belong to the author.
