Mastering Machine Learning with Spark 2.x

Modelling data with Random Forest

Random Forest is an algorithm which can be used for different problems - binomial as we showed in the previous chapter, regression, or multiclass classification. The beauty of Random Forest is that it combines multiple weak learners represented by decision trees into one ensemble.

Furthermore, to reduce variance of individual decision trees, the algorithms use the concept of bagging (Bootstrap aggregation). Each decision tree is trained on a subset of data generated by random selection with replacement.

Do not confuse bagging with boosting. Boosting incrementally builds an ensemble by training each new model to emphasize observations that previous model misclassified. Typically, after a weak model is added ...