Using Ensemble Learning and Random Forest Techniques to Solve Complex Problems

Abstract

The branch of computer science and artificial intelligence known as machine learning is used to program machines to learn. Algorithms for machine learning are software programs or methods used to find hidden patterns in data, predict outcomes, and improve performance based on past performance. A technique used in machine learning called ensemble learning combines several models, such as classifiers or experts that have been carefully constructed to solve a particular computational intelligence problem. Ensemble refers to a collaborative effort to create a single impact. An ensemble can predict events more accurately and perform better in general than a single contributor. A random forest is a technique for ensemble learning in which many decision trees are combined to create the forest. This chapter covers the fundamentals of ensemble learning using random forest, implementation with real-world examples, and developing a model.