One-Hot-Encoding for features! I have a question about One-Hot-Encoding, something confusing me.:\
I have this sample dataset. My dataset is categorical:




F1
F2
F3
F4
Target




1
Blue
3
Car
Yes


4
Red
6
Ship
No


3
Pink
3
Cow
Yes


9
Black
6
Fish
Yes



question:
Should I use the One-Hot-Encoding for F2 and F4 too?
This is clear for me, that must be used one hot encoding for column Target. But what about the Features?
If the answer is yes why and if the answer is no why?
Thanks for your support.
Correct,  all categorical features should be encoded into binary digits so ML algo have more predictive power as categorised features cannot have order or magnitude ( be careful also about
multicollinearity if you use regression)
Some ML framework such as Catboost automatically encode features for you if you specify the feature index. I also like using python patsy to create input matrices