algorithm

Decision trees are a fundamental building block in machine learning, particularly in the context of ensemble methods like Random Forest. A decision tree is a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. In machine learning, decision trees are used to classify or predict outcomes based on a set of input features.   Recursive Induction: The Core Process The process of building a decision tree is known as recursive partitioning or recursive induction. It involves the following steps: Key Concepts in Decision Tree Induction Advantages of Decision Trees Limitations of Decision Trees Conclusion Recursive induction is a powerful technique for building decision trees. By understanding the principles of feature selection, splitting criteria, and stopping conditions, you can effectively construct accurate and interpretable decision trees. While decision trees can be used as standalone models, they are often combined with other techniques like bagging and boosting to create more robust and powerful ensemble models like Random Forest.