Otsustamispuud

Otsustamispuud, often translated as "decision trees," are a supervised machine learning algorithm used for both classification and regression tasks. They work by recursively partitioning the data into smaller and smaller subsets based on the values of input features. The structure resembles a tree, with an internal node representing a test on an attribute, a branch representing the outcome of the test, and a leaf node representing 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. Common criteria for selecting the best split include Gini impurity and information gain. Gini impurity measures the probability of incorrectly classifying a randomly chosen element if it were randomly labeled according to the distribution of labels in the subset. Information gain, on the other hand, measures the reduction in entropy (a measure of disorder) achieved by splitting the data on a particular attribute. The algorithm continues this splitting process until a stopping criterion is met, such as reaching a maximum depth or when a node contains instances of only one class.

Decision trees are popular due to their interpretability. The resulting tree can be easily visualized and understood, making it a valuable tool for explaining predictions. They are also relatively fast to train and can handle both numerical and categorical data. However, they can be prone to overfitting, meaning they may learn the training data too well and perform poorly on unseen data. Techniques like pruning or ensemble methods such as random forests and gradient boosting are often used to mitigate overfitting and improve performance.