Which data to plot to know what model suits best for the problem?
If you are using off-the-shelf packages like sklearn, then many simple models like SVM, RF, etc, are just one-liners, so in practice, we usually try several such models at the same time.
I'm sorry, i know that this is a very basic question but since i'm still a beginner in machine learning, determining what model suits best for my problem is still confusing to me, lately i used linear regression model (causing the r2_score is so low) and a user mentioned i could use certain model according to the curve of the plot of my data and when i see another coder use random forest regressor (causing the r2_score 30% better than the linear regression model) and i do not know how the heck he/she knows better model since he/she doesn't mention about it. I mean in most sites that i read, they shoved the data to some models that they think would suit best for the problem (example: for regression problem, the models could be using linear regression or random forest regressor) but in some sites and some people said firstly we need to plot the data so we can predict what exact one of the models that suit the best. I really don't know which part of the data should i plot? I thought using seaborn pairplot would give me insight of the shape of the curve but i doubt that it is the right way, what should i actually plot? only the label itself or the features itself or both? and how can i get the insight of the curve to know the possible best model after that?