A decision tree is a supervised machine learning algorithm that is used for both classification and regression tasks. It’s a popular tool for decision-making and predictive modeling because it can handle both categorical and numerical data, is easy to understand, and can be visualized effectively. Decision trees are constructed through a process of recursive partitioning, where the dataset is repeatedly split into subsets based on certain criteria or features.
How a Decision Tree Is Built:
- Feature Selection: The algorithm selects the best feature from the dataset to split the data at each internal node. The goal is to choose the feature that best separates the data into homogeneous groups, minimizing impurity or error.
- Splitting Criteria: The algorithm uses a splitting criterion to determine how to divide the data based on the chosen feature. Common splitting criteria for classification tasks include Gini impurity and entropy, while for regression tasks, it’s often mean squared error (MSE).
- Recursive Process: The process of feature selection, splitting, and creation of child nodes is repeated recursively until a stopping criterion is met. This can include a maximum tree depth, a minimum number of samples required to create a node, or a maximum number of leaf nodes.