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Hands-On Machine Learning for Algorithmic Trading by Stefan Jansen

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Pros and cons of random forests

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 ...

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