Questions to ask when deciding how to approach predictive problems

Jessica YungData ScienceLeave a Comment

  1. Is the situation stochastic or deterministic?
  2. Is it time-inhomogeneous? (Different across time?)
  3. How much data do you have available?
  4. What limitations are there with respect to computational cost (compute and time), both for training and predicting?
  5. Do you need to try actions to learn about situations? (If so, consider Reinforcement Learning.)
  6. Do your actions have an impact on the environment? (If not, Reinforcement Learning may be a poor choice because you are just implicitly learning to predict future variables using weaker reinforcement signals compared to the supervised learning case.)

(Non-exhaustive list)

References: Bayes-Ian, cesarsalgado on Reddit

Context: When doing research for my Machine Learning in Trading project, I came across a Reddit thread on applying (A) DeepMind’s deep learning methods for training a computer to play Atari to (B) the problem of predicting stock prices. This thread contained comments on why that set of reinforcement learning methods would be a bad fit for predicting stock prices. I generalised those comments and put them in the list above.

Leveraging Google DeepMind software and Deep Learning to play the stock market from MachineLearning

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