Things to know about machine learning (ML)
Machine learning is widely used in computer science and other fields such as compliance. Developing successful machine learning applications, however, requires a substantial amount of "black art" that is difficult to find in textbooks. Here come the 12 key lessons that machine learning researches have learned.
12 key lessons about ML
- Learning = Representation + Evaluation + Optimization.
- It is 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.
