"决策树学习任务及算法概述：分类、回归、聚类、监督、无监督、半监督、强化学习"

's decision tree full PPT courseware contains a comprehensive overview of decision tree models for both classification and regression tasks. Common algorithms such as CART, ID3, and C4.5 are frequently utilized in this field. Decision tree learning aims to create a generalized model that can accurately predict outcomes for new and unseen examples. The training data used can be either labeled or unlabeled, falling within the spectrum of supervised, unsupervised, and semi-supervised learning. The tree structure consists of root nodes, leaf nodes representing decision results, and internal nodes that correspond to attribute tests. The Hunt algorithm is commonly employed in the process of decision tree learning. Overall, decision tree modeling is a powerful tool in data analysis and predictive modeling that can provide valuable insights and solutions for various real-world problems.'