Some notes from machine-learning-projects of coursera

1. do best each steps of machine learning systems
   1. train
   2. dev/test
   3. real world
2. if the real-world failed, check again all previous steps
3. Always create a SINGLE NUMBER metric to quickly choose which machine learning algorithm is best.
4. If the current metrics (i.e., precision, recall) cannot capture the aforementioned number, create a new number, or create a new optimization matrics + new satisfaction, for example, there are Presion, Recall, and Running time. The optimization matrix is [Precision, Recall] subject to the satisfaction that the running time is below number T. All the algorithms which have running time longer than T is discard completely, without reconsider.
5. Always look at the purpose of machine learning tasks and aim for it. Changes things to make that one works, do not use the metric which is nice to see but cannot capture the goals correctly.