Final answer:
One can control the data-fit complexity of a regression tree by adjusting hyperparameters such as the maximum depth of the tree, minimum samples split, and minimum sample leaf to prevent overfitting or underfitting.
Step-by-step explanation:
When working with regression trees, controlling the data-fit complexity is crucial to avoid overfitting or underfitting the model. One of the key hyperparameters that we can adjust to control this complexity is the maximum depth of the tree. The maximum depth determines the length of the longest path from the root node to a leaf. By limiting the depth, we can prevent the model from becoming overly complex and memorizing the training data, thus improving its generalization to new data.
Another hyperparameter is the minimum samples split, which specifies the minimum number of samples required to split an internal node. Higher values prevent the creation of nodes that contain too few samples, which can lead to overfitting. Conversely, a very low value can result in splitting nodes that do not provide meaningful information gain.
Finally, minimum sample leaf is another hyperparameter that can be tuned. It represents the minimum number of samples required to be at a leaf node. A higher value creates a more generalized model but may lead to underfitting if set too high.