Döntésfák

Döntésfák, or decision trees, are a supervised machine learning technique used for both classification and regression tasks. They are structured as a tree-like graph where internal nodes represent tests on an attribute, branches represent the outcome of the test, and leaf nodes represent a class label (in classification) or a continuous value (in regression). The process of using a decision tree involves starting at the root node and traversing down the tree by following the branches that correspond to the values of the data's attributes. This continues until a leaf node is reached, which then provides the prediction.

The construction of a decision tree involves recursively partitioning the data based on the attribute that best separates the data into distinct classes or predicts the target variable most effectively. Common algorithms for building decision trees include ID3, C4.5, and CART. These algorithms employ impurity measures like Gini impurity or information gain to determine the best attribute to split on at each node.

Decision trees are popular due to their interpretability; the decision-making process can be easily visualized and understood. They can handle both numerical and categorical data, and they do not require feature scaling. However, they can be prone to overfitting, especially with complex datasets, leading to trees that are too deep and do not generalize well to unseen data. Techniques like pruning or using ensemble methods such as random forests and gradient boosting are often employed to mitigate overfitting and improve performance.