A Few Useful Things to Know About Machine Learning | Tapping into the "folk knowledge" needed to advance machine learning | Pedro Domingos | Communications of the ACM | vol. 55 no. 10 | October 2012

Pedro Domingos summarizes 12 key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.

• 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