Optimizing Random Forest with Apache Spark: A Survey on Distributed Machine Learning and Big Data Scalability

Current trends have contributed to the rapid development of Big Data Processing Technologies. As one of the first variants of machine learning algorithms, random forests have exceptional classification and elaboration characteristics. The field of application in many applications has gradually expanded, and traditional random forests have been threatened, especially in large-scale data set processing. The scope of application is limited, and high computational costs and long training time are especially important. As a distributed computing-based platform in RAM, Apache Spark can optimize the learning speed and cost of developing random noise algorithms in a big data environment. This article discusses the random noise optimization strategy based on Apache Spark.it not only deals with parallel computing, but also discusses the techniques of object selection optimization and model improvement. At the same time, it outlines the latest achievements in the field of random forest optimization based on Spark and considers possible directions of its further development.