Which are the most important parameters to tune for k-NN, NB, RF, DL, SVM for text classification?

Dear community,

I would like to compare the performance of the following five algorithms on different text classification tasks*:

1. k-Nearest Neighbors (k-NN)
2. Naive Bayes (NB)
3. Random Forest (RF)
4. Deep Learning (DL)
5. Support Vector Machines (SVM)

Question 1: Which paramesters are the most important to optimize for each method 1-5?

Question 2: What ranges should I give those parameters in the parameter optimization operator in order to avoid "boiling the ocean"?

Thanks in advance!

* each task has between 3 to 5 classes and the text length varies between 3 to 70 words per document / example