Decision Tree

A decision tree is a graphical representation used for making decisions and predictions based on various input variables. It models decisions and their possible consequences, including chance event outcomes, resource costs, and utility. The tree structure begins with a single node (the root) and branches out to represent different choices or outcomes, culminating in leaf nodes that signify the final decisions or classifications.In a decision tree, each internal node represents a feature or attribute of the data, each branch represents a decision rule, and each leaf node represents an outcome or class label. Decision trees are commonly used in machine learning for classification and regression tasks because they provide a straightforward and interpretable model. They can handle both numerical and categorical data and do not require extensive data normalization. Overall, decision trees are a fundamental tool in data analysis and predictive modeling, allowing users to visualize and understand the decision-making process.