What should be the depth of trees in Random Forest and boosting?

In Random Forest, the depth of trees is usually chosen to be sufficiently large so that the trees can effectively split the data and capture complex dependencies. Often, trees are grown to their maximum depth (until few objects remain in a leaf), which helps reduce bias, and averaging many trees decreases variance.

In boosting (for example, Gradient Boosting), trees are usually small — with depths of 3-5 levels. This is because boosting builds trees sequentially, correcting the errors of previous trees. Small trees help avoid overfitting and make the model more robust.

Thus:

• Random Forest: deep trees (often without limiting depth).
• Boosting: shallow trees (3-5 levels).

The exact depth choice depends on the task, data volume, and the model's generalization requirements.

In Random Forest and boosting, the depth of trees is an important hyperparameter that affects the model quality and overfitting.

• Random Forest: Usually, relatively shallow trees are used, but the depth can vary. Trees are often built to the maximum depth (without restriction) or with a depth limit in the range of 10-30 levels. Deep trees allow for better data separation but can lead to overfitting. Random Forest reduces overfitting by averaging a large number of trees.

• Gradient boosting (e.g., XGBoost, LightGBM): Very shallow trees are typically used here — often limited to 3-8 levels. This is because boosting builds trees sequentially, correcting errors of previous trees, and shallow trees help avoid overfitting and improve generalization.

Thus, in Random Forest, the tree depth can be greater, while in boosting, it is usually smaller to ensure more stable training and better generalization.