A decision tree in statistical analysis is a graphical representation of a decision-making process. It is a tree-like model where each internal node represents a decision or test on an attribute, each branch represents an outcome of that test, and each leaf node represents a class label or a decision. Decision trees are used for both classification and regression analysis.
In a decision tree for classification, the goal is to classify an instance into one of several predefined classes. The tree is built by recursively splitting the data based on the most significant attribute at each node, creating decision rules that lead to the final classification.
In regression decision trees, the goal is to predict a continuous variable instead of a class label. The process is similar, with each node representing a decision based on an attribute, and the leaves containing the predicted values.
Decision trees are popular in machine learning and statistical analysis because they are easy to understand and interpret. They provide a visual representation of decision-making processes and can be a powerful tool for both analysis and prediction.