Pedro Domingos教授在其著名的文章《A Few Useful Things to Know about Machine Learning》中提到以下內(nèi)容：

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

詳情請參閱 《A Few Useful Things to Know about Machine Learning》.

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轉(zhuǎn)載于:https://www.cnblogs.com/statml/archive/2013/04/04/2999030.html

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