12 Useful Tips for Machine Learning

Pedro Domingos of the Department of Computer Science and Engineering at the University of Washington provides a very useful paper with tips for machine learning. The paper is title, A Few Useful Things to Know about Machine Learning [pdf].

Below are the 12 useful tips.

  1. LEARNING = REPRESENTATION + EVALUATION + OPTIMIZATION
  2. IT’S GENERALIZATION THAT COUNTS
  3. DATA ALONE IS NOT ENOUGH
  4. OVERFITTING HAS MANY FACES
  5. INTUITION FAILS IN HIGH DIMENSIONS
  6. THEORETICAL GUARANTEES ARE NOT WHAT THEY SEEM
  7. FEATURE ENGINEERING IS THE KEY
  8. MORE DATA BEATS A CLEVERER ALGORITHM
  9. LEARN MANY MODELS, NOT JUST ONE
  10. SIMPLICITY DOES NOT IMPLY ACCURACY
  11. REPRESENTABLE DOES NOT IMPLY LEARNABLE
  12. CORRELATION DOES NOT IMPLY CAUSATION

For details and a good explanation of each, see the paper A Few Useful Things to Know about Machine Learning [pdf].

Also,later this year, Pedro Domingos will be teaching a machine learning course via Coursera. Sign up if you are interested.

One thought on “12 Useful Tips for Machine Learning”

Leave a Reply

Your email address will not be published. Required fields are marked *