Advantages vs Disadvantages of Random forests

Advantages: • They are generally more accurate than single trees • The process can be easily parallelized • They also provide additional information, like ranking of the mos influential features (useful in unsupervised learning) • No need to data preprocessing (like trees) Disadvantages: • They sacrifice readability • Can be computationally intensive