A decision tree, a form of machine learning system, employs a tree-like structure to depict decisions and their potential outcomes. The algorithm recursively partitions the dataset based on specific attribute values, starting from the root node containing all the data. Each decision node signifies an attribute test, while each leaf node represents the ultimate choice or result. The objective of the splitting criteria is to optimize both heterogeneity within and between groupings. The transparency and interpretability of decision trees enhance the understanding of the model’s decision-making process. To address the tendency toward overfitting, pruning is a method commonly used. Popular techniques for building decision trees include CART, C4.5, and ID3, with applications in both regression and classification tasks. Decision trees also serve as the foundation for ensemble techniques like Gradient Boosting and Random Forests, improving predictive performance.
Here’s an overview of how a decision tree operates:
- Root Node: Represents the entire dataset, divided into subgroups based on the selected attribute’s value.
- Decision Nodes (Internal Nodes): Nodes representing decisions based on attribute values, featuring branches leading to child nodes with various attribute values.
- Leaf Nodes: Terminal nodes signifying the ultimate choices or results. In classification tasks, each leaf node is associated with a specific class label; in regression tasks, it corresponds to a numerical value.
- Splitting Criteria: The algorithm selects the feature that optimally divides the data into homogeneous groups at each decision node, aiming to maximize both subset heterogeneity and homogeneity.
- Recursive Process: The splitting process is applied recursively to each subset to create a tree structure. This continues until a specified point is reached, such as a certain depth, a minimum sample requirement in a node, or when homogeneity cannot be further improved.