ákvarðunartré

ákvarðunartré, also known as a decision tree, is a supervised machine learning algorithm that can be used for both classification and regression tasks. It works by recursively partitioning the input data into smaller and smaller subsets based on the values of input features. Each internal node in the tree represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label (in classification) or a continuous value (in regression).

The process of building a decision tree involves selecting the best attribute to split the data at each node. This selection is typically based on criteria such as information gain or Gini impurity, which measure how well an attribute separates the data into distinct classes. The algorithm continues to split the data until a stopping criterion is met, such as reaching a maximum depth, a minimum number of samples in a leaf node, or when no further improvement in purity can be achieved.

Once a decision tree is built, it can be used to make predictions. For a new data point, it is passed down the tree, following the branches corresponding to its feature values, until it reaches a leaf node. The prediction is then the class label or value associated with that leaf node.

Decision trees are popular due to their interpretability, as the tree structure can be easily visualized and understood. However, they can be prone to overfitting, meaning they may perform well on the training data but poorly on unseen data. Techniques like pruning are often used to mitigate this issue.