Päätöksentekopuu

Päätöksentekopuu, or decision tree in English, is a flowchart-like structure used in decision analysis and machine learning. It represents a series of decisions, their possible outcomes, and the associated consequences. Each internal node of the tree signifies a test on an attribute, each branch represents an outcome of the test, and each leaf node represents a class label or a decision.

Decision trees are widely used for classification and regression tasks. In classification, the tree aims to predict a categorical target variable. For example, a decision tree could be used to predict whether a customer will buy a product based on their age, income, and past purchase history. In regression, the tree predicts a continuous target variable. An example would be predicting house prices based on features like size, location, and number of bedrooms.

The construction of a decision tree involves recursively partitioning the data into subsets based on the values of input attributes. Various algorithms exist for building decision trees, such as ID3, C4.5, and CART. These algorithms typically use criteria like information gain or Gini impurity to select the best attribute to split on at each node. Pruning is often employed to prevent overfitting and improve the generalization performance of the tree.

Decision trees are known for their interpretability, as the decision-making process can be easily visualized and understood. However, they can also be prone to creating biased trees if some classes dominate. Despite this, their simplicity and effectiveness make them a valuable tool in various fields, including data mining, pattern recognition, and artificial intelligence.