In this chapter, we had a brief introduction to powerful tree-based algorithms, such as DTs, GBT, and RF, for solving both classification and regression tasks. We saw how to develop these classifiers and regressors using tree-based and ensemble techniques. Through two real-world classification and regression problems, we saw how tree ensemble techniques outperform DT-based classifiers or regressors.
We covered supervised learning for both classification and regression on structured and labeled data. However, with the rise of cloud computing, IoT, and social media, unstructured data is growing unprecedentedly, giving more than 80% data, most of which is unlabeled.
Unsupervised learning techniques, such as clustering analysis and dimensionality reduction, are key applications in data-driven research and industry settings to find hidden structures from unstructured datasets...
