As a rookie to machine learning area, I tried to play some Data Science tutorials and beginner competitions to gain some knowledge and experience.
The problem I encountered in every scenarios is that, after choosing an suitable model and searching for optimal hyper parameters. I don't know what I can do to improve the precision or performance.
There are always context specific wisdom behind every tasks to discover useful information. But I wonder if there is a systematically way or a heuristic which can apply on general cases.
I heard of "Feature Engineering" and read some articles about it, but I still don't know how can I apply the knowledge.
The only technique I learned is Recursive Feature Elimination, but usually it only brings little improvement.
When the feature is anonymous or the feature has thousands of dimensions, what's the general strategy to deal with the features?
Is there other directions I can work on?