Random forests can overfit the data primarily due to excessive model complexity, especially when individual trees are grown too deep. Specifically:

• Excessive tree depth: When the max depth parameter of trees in the forest is set very high (e.g., 10,000), each tree splits until all nodes are pure, creating tiny, highly specific decision regions tailored to the training data. This leads to overfitting because these detailed splits capture noise and peculiarities in the training set that do not generalize to new data

• High model complexity relative to data size: If the training dataset is small or limited, a complex model like a deep random forest can memorize training examples instead of learning generalizable patterns, causing overfitting

• Noisy or irrelevant training data: Noise in the data can cause the model to fit irrelevant patterns, increasing the risk of overfitting

• Long training or insufficient regularization: Although random forests typically avoid overfitting by averaging many trees, if trees are fully grown without constraints or if the model complexity is too high, overfitting can still occur

In summary, random forests overfit mainly when individual trees are grown without depth limits, causing them to memorize training data noise and details, especially when training data is limited or noisy. Proper tuning of tree depth and other parameters, along with sufficient and clean data, helps prevent this overfitting