How does a random forest reduce the influence of individual trees on the final prediction?

Random forest reduces the influence of individual trees on the final prediction through two key mechanisms:

1. Bagging (Bootstrap Aggregating) — each tree is trained on a random sample with replacement from the original data. This ensures diversity among the trees, as they see different subsets of data.

2. Random feature selection at node splits — when building each node, a random set of features is chosen, and the best one for splitting is selected. This further reduces correlation between the trees.

As a result, the final prediction is formed by averaging (for regression) or voting (for classification) the results of all trees. This compensates for errors and overfitting of individual trees, increasing the overall robustness and accuracy of the model.