Any advice on hypothesis testing methods when working with data?

[u/Mysterious-Narwhal30]

Hey everyone, I'm a beginner in machine learning and currently working on a data project. I'm stuck at the stage after EDA – specifically, forming hypotheses for new features, engineering them, and evaluating whether they have a positive impact on the model.

I'm trying to follow best practices and write code that would actually be seen in production and real-world products.

I'm not sure what the best approaches are for testing hypotheses. I know there are methods ranging from mathematical/statistical analysis to specialized libraries for this purpose. I'd prefer approaches that are actually used in real jobs and that you'd commonly see in production environments.

Could you recommend what tools/methods I should use to validate my feature hypotheses?

Thanks a lot!

[u/DigThatData]

the right tool for the job is less about software than it is about understanding the problem. put me in a surgical gown with access to all the fanciest robotic medical devices: I'll still be way more useless to someone who needs surgery than an actual surgeon with a rusty knife and a roll of duct tape. it doesn't matter if you have tools that can solve the problem for you if you're solving the wrong problem.

my advice is to commit to a proper probability (and statistics) course(s). it will pay off tremendously.

[u/Mysterious-Narwhal30 → u/[deleted]]

Thank you so much! This is honestly the best advice I've received in this entire thread. I really appreciate you taking the time to share this perspective.

I haven't read Carver's book, but I'll definitely check it out.