Scala and Spark for Big Data Analytics

Improving classification accuracy using random forests

Random forests (also sometimes called random decision forests) are ensembles of decision trees. Random forests are one of the most successful machine learning models for classification and regression. They combine many decision trees in order to reduce the risk of overfitting. Like decision trees, random forests handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture nonlinearities and feature interactions. There are numerous advantageous RFs. They can overcome the overfitting problem across their training dataset by combining many decision trees.

A forest in the RF or RDF usually consists of hundreds of thousands ...