04-Summary.Rmd

# Summary
## Classification Problem
What are some statistical properties of two families of classifiers built from data?



### Similarity Classifiers



### Emprirical Risk Minimization




## No Free Lunch Theorem

"Pointwise Statement":

"Uniform Statement": 


Example of Pointwise Statement vs Uniform Statement:


No Free Lunch Theorem 1:


Practical Corollary:


No Free Lunch Theorem 2:

Practical Corollary:

Observation: we can not quantify the universal convergence rate.

Conclusion: No magic classifier that suits every situation.