Decision Tree
Decision trees are a fundamental machine learning algorithm used for both classification and regression tasks. Introduced in the 1960s, they work by recursively splitting data based on feature values, creating a tree-like structure where each internal node represents a decision based on a feature, and each leaf node represents an outcome or prediction. Decision trees are highly interpretable, as they explicitly map out decision rules in a hierarchical structure, making them a popular choice in situations where model transparency is crucial.
https://en.wikipedia.org/wiki/Decision_tree_learning
Decision tree algorithms, such as CART (Classification and Regression Trees) or ID3, use measures like Gini impurity, entropy, or mean squared error to determine the best feature and threshold to split on at each node. A major advantage of decision trees is their ability to handle both numerical and categorical data, making them versatile for various datasets. However, they tend to overfit the training data, especially if the tree is too deep, leading to poor generalization to new data. To combat this, techniques like pruning or ensemble methods (e.g., Random Forest or Gradient Boosting), which combine multiple trees, are often used to improve performance.
https://scikit-learn.org/stable/modules/tree.html
While decision trees are simple and easy to understand, they are often sensitive to small changes in the data. This can cause instability, where a slight variation in input can result in a vastly different tree. Despite this, they remain a foundational tool in machine learning, with applications in fields such as finance, healthcare, and marketing. Their ability to provide interpretable decision rules and their flexibility in handling complex datasets make them essential components in the toolkit of any data scientist.