Bagged ensemble models have both advantages and disadvantages. The advantages of random forests include:

• The predictive performance can compete with the best supervised learning algorithms
• They provide a reliable feature importance estimate
• They offer efficient estimates of the test error without incurring the cost of repeated model training associated with cross-validation

On the other hand, random forests also have a few disadvantages:

• An ensemble model is inherently less interpretable than an individual decision tree
• Training a large number of deep trees can have high computational costs (but can be parallelized) and use a lot of memory
• Predictions are slower, which may create challenges for applications that require low latency