Which parameter is typically set to prevent overfitting in regression trees?

In regression trees, preventing overfitting is crucial to ensure that the model generalizes well to unseen data rather than just memorizing the training dataset. Various parameters can be adjusted to help mitigate this risk.

One such parameter is the maximum tree depth, which limits how many levels the tree can grow. A deeper tree can model more complex relationships and may fit the training data very closely, but this increases the likelihood of capturing noise as well as signal, leading to overfitting.

Another important parameter is the minimum node size, which determines the smallest permissible number of samples required to create a child node. By setting this parameter, you can ensure that the model does not create overly complex splits that are based on very few observations, which can again contribute to overfitting.

Additionally, the number of features that can be used per split is also a relevant factor. Restricting the number of features can prevent the model from becoming too complex and sensitive to variations in the training data.

Therefore, all these parameters—maximum tree depth, minimum node size, and the number of features per split—play a vital role in controlling the complexity of the regression tree. When these parameters are carefully tuned, they work together to reduce the risk of overfitting