In this section, we'll see how to use RF to solve both regression and classification problems. We'll use DT implementation from the Spark ML package in Scala. Although both GBT and RF are ensembles of trees, the training processes are different. For instance, RF uses the bagging technique to perform the example, while GBT uses boosting. Nevertheless, there are several practical trade-offs between both the ensembles that can pose a dilemma about what to choose. However, RF would be the winner in most of the cases. Here are some justifications:
- GBTs train one tree at a time, but RF can train multiple trees in parallel. So the training time is lower with RF. However, in some special cases, training and using a smaller number of trees with GBTs is faster and more convenient.
- RFs are less prone to overfitting. In other words, RFs reduces...
