I am a beginner in the machine learning field and I want to make sure of somethings after I watched the first 3 lectures.
1- In the first lecture, the first model that was created was a Random Forest (RF) because it does not need specific preparation of the data, right?
2- can I consider starting each project with RF as a first general step?
3- in the 4th lectures (RF interpretation and feature importances), he got the feature importances from the RF. my question is, Do I need to create a random forest with its default parameters to get the importances or to try to get the best parameters for RF before getting them?
4- After I get the important features, should I recreate my train and valid sets from the original data by selecting only those features or continue using my first split (which I used to get the important features)?
and thank you for your help