döntésfát

Döntésfa is a Hungarian term that translates to "decision tree" in English. In the context of computer science and artificial intelligence, a decision tree is a supervised learning algorithm used for both classification and regression tasks. It is a flowchart-like structure where each internal node represents a test on an attribute, each branch represents an outcome of the test, and each leaf node represents a class label (in classification) or a continuous value (in regression). The decision tree recursively partitions the data into subsets based on the values of input attributes. The process of building a decision tree involves selecting the best attribute to split the data at each node, typically using criteria such as information gain or Gini impurity. Once the tree is built, predictions are made by traversing the tree from the root node down to a leaf node based on the values of the input features. Decision trees are popular due to their interpretability and ability to handle both numerical and categorical data. However, they can be prone to overfitting, and techniques like pruning are often employed to mitigate this issue. Ensemble methods like Random Forests and Gradient Boosting further enhance the performance of decision trees by combining multiple trees.