In the previous chapter, we solved both classification and regression problems using linear models. We also used logistic regression, support vector machine, and Naive Bayes. However, in both cases, we haven't experienced good accuracy because our models showed low confidence.
On the other hand, tree-based and tree ensemble classifiers are really useful, robust, and widely used for both classification and regression tasks. This chapter will provide a quick glimpse at developing these classifiers and regressors using tree-based and ensemble techniques, such as decision trees (DTs), random forests (RF), and gradient boosted trees (GBT), for both classification and regression. More specifically, we will revisit and solve both the regression (from Chapter 2, Scala for Regression Analysis) and classification (from Chapter 3, Scala for Learning...
