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.
- LEARNING = REPRESENTATION + EVALUATION + OPTIMIZATION
- IT’S GENERALIZATION THAT COUNTS
- DATA ALONE IS NOT ENOUGH
- OVERFITTING HAS MANY FACES
- INTUITION FAILS IN HIGH DIMENSIONS
- THEORETICAL GUARANTEES ARE NOT WHAT THEY SEEM
- FEATURE ENGINEERING IS THE KEY
- MORE DATA BEATS A CLEVERER ALGORITHM
- LEARN MANY MODELS, NOT JUST ONE
- SIMPLICITY DOES NOT IMPLY ACCURACY
- REPRESENTABLE DOES NOT IMPLY LEARNABLE
- 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.
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