raajausmenetelmät

Raajausmenetelmät, or pruning methods, are techniques used to reduce the size of decision trees or other machine learning models. The primary goal of raajausmenetelmät is to prevent overfitting, a phenomenon where a model learns the training data too well, including its noise and irregularities, leading to poor performance on new, unseen data. By removing redundant or less informative branches from a tree, these methods aim to create a more generalized and robust model.

There are broadly two categories of raajausmenetelmät: pre-pruning and post-pruning. Pre-pruning involves stopping the growth of the decision tree before it becomes fully grown. This can be achieved by setting criteria such as a maximum depth for the tree, a minimum number of samples required to split a node, or a minimum number of samples required in a leaf node. If a node meets these criteria, it is not split further. Post-pruning, on the other hand, involves growing the tree to its full potential and then removing branches that are deemed unnecessary. This is often done by evaluating the performance of subtrees and removing those that do not significantly improve or even degrade the model's accuracy on a validation set.

Common post-pruning techniques include cost-complexity pruning, where a penalty is introduced for complexity, and reduced-error pruning, which iteratively removes nodes that do not improve accuracy on a separate pruning set. The choice of raajausmenetelmät depends on the specific dataset, the complexity of the problem, and the desired trade-off between model accuracy and complexity. Effective raajausmenetelmät are crucial for building reliable predictive models.