päätöspuilla

Päätöspuilla, often translated as "decision trees," are a fundamental tool in machine learning and data mining. They are a type of supervised learning algorithm used for both classification and regression tasks. A decision tree visually represents a series of decisions and their possible outcomes.

The structure of a decision tree consists of nodes and branches. The root node represents the entire dataset. Each internal node represents a test on an attribute, such as asking a question about a specific feature of the data. Each branch represents the outcome of the test, leading to another node or a leaf node. Leaf nodes, also known as terminal nodes, represent the final decision or prediction, which can be a class label in classification or a continuous value in regression.

The process of building a decision tree involves recursively partitioning the dataset based on the attribute that best separates the data at each step. Various algorithms exist for constructing decision trees, such as ID3, C4.5, and CART. These algorithms use criteria like information gain or Gini impurity to determine the best attribute to split on.

Decision trees are popular due to their interpretability. The tree structure makes it easy to understand the decision-making process. They can handle both numerical and categorical data, and they do not require data normalization. However, they can be prone to overfitting, meaning they might create overly complex trees that do not generalize well to new, unseen data. Techniques like pruning are used to mitigate this issue.