In this section let us explore briefly two kinds of ensemble methods for decision trees: random forests and gradient boosting.
Decision tree-based ensemble methods
Random forests
Random forests is a technique where you construct multiple trees, and then use those trees to learn the classification and regression models, but the results are aggregated from the trees to produce a final result.
Random forests are an ensemble of random, uncorrelated, and fully-grown decision trees. The decision trees used in the random forest model are fully grown, thus, having low bias and high variance. The trees are uncorrelated in nature, which results in a maximum decrease in the variance. By uncorrelated...
