päätöspuumenetelmät

Päätöspuumenetelmät, often translated as decision tree methods, are a class of supervised machine learning algorithms used for both classification and regression tasks. They work by recursively partitioning the dataset into smaller and smaller subsets based on the values of input features. The resulting structure resembles a tree, with internal nodes representing tests on features, branches representing the outcomes of these tests, and leaf nodes representing the final predictions.

The process begins at the root node, where the algorithm selects the feature and the split point that best divides the data according to some criterion, such as Gini impurity or information gain for classification, or variance reduction for regression. This process is repeated for each resulting subset, creating child nodes. The recursion continues until a stopping condition is met, such as a maximum tree depth, a minimum number of samples in a leaf node, or when no further improvement can be made.

Once the tree is built, a new data point is classified or predicted by traversing the tree from the root node, following the branches based on its feature values until a leaf node is reached. The prediction at the leaf node is then assigned to the data point.

Decision trees are interpretable and can handle both numerical and categorical data. 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, setting minimum sample requirements for splits, and ensemble methods such as random forests and gradient boosting are used to mitigate overfitting and improve the generalization performance of decision tree-based models.