Which statements regarding boosting, random forests, and bagging are false?

Boosting is indeed designed to reduce bias mainly, while it can also help reduce variance, but its primary strength is in addressing bias through sequentially focusing on the errors made by prior models. Therefore, the assertion that boosting reduces both variance and bias can be misleading, making it plausible to consider that as a false statement.

On the other hand, it is well-understood that overfitting can occur in random forests due to having too many trees, but it is not restricted to just that scenario. With a significant number of trees, randomness in feature selection and averaging predictions usually helps combat overfitting. Thus, the idea that it is only a concern in the context of many trees offers an incomplete perspective.

Random forests are indeed based on bagging techniques, utilizing multiple bootstrap samples and averaging model predictions from decision trees built on these samples. This relationship between random forests and bagging highlights the evolutionary aspect of random forests rather than suggesting random forests enhance performance in comparison.

Bagging, particularly with decision trees, can significantly improve model performance through variance reduction compared to single decision trees, but often boosting can achieve better performance when properly tuned. So the assertion that bagging enhances performance considerably compared to boosting does not hold universally, making it a questionable claim.

Overall