In this chapter, you have learned the complete details about tree-based models, which are currently the most used in the industry, including individual decision trees with grid search and an ensemble of trees such as bagging, random forest, boosting (including AdaBoost, gradient boost and XGBoost), and finally, ensemble of ensembles, also known as model stacking, for further improving accuracy by reducing variance errors by aggregating results further. In model stacking, you have learned how to determine the weights for each model, so that decisions can be made as to which model to keep in the final results to obtain the best possible accuracy.
In the next chapter, you will be learning k-nearest neighbors and Naive Bayes, which are less computationally intensive than tree-based models. The Naive Bayes model will be explained with an NLP use case. In fact, Naive Bayes and SVM are often used where variables (number of dimensions) are very high in number to classify.
