päätöspuussa

Päätöspuu is a Finnish term that translates to "decision tree" in English. It refers to a supervised learning algorithm used for classification and regression tasks. A decision tree works by recursively partitioning the data into subsets based on the values of input features. The process starts at the root node, where a test is performed on a particular feature. Based on the outcome of this test, the data is directed down one of the branches to a child node. This process continues at each node, with new tests being applied to different features until a leaf node is reached. Leaf nodes represent the final outcome, which can be a class label in classification problems or a continuous value in regression problems.

The structure of a decision tree visually resembles an inverted tree, with nodes representing tests, branches representing the outcomes of those tests, and leaves representing the predictions. Various algorithms exist for constructing decision trees, such as ID3, C4.5, and CART. These algorithms differ in how they select the best feature to split on at each node, often using metrics like information gain or Gini impurity. Decision trees are known for their interpretability, as the decision-making process can be easily visualized and understood. However, they can be prone to overfitting, meaning they might learn the training data too well and perform poorly on unseen data. Techniques like pruning are employed to mitigate this issue.