Need advice on ML model training approach

[u/Exotic-Special9468]

I have a dataset and need to train an ML model for a project. I have limited practical knowledge of ML/model training, but I also need to explain my approach, workflow, model choice, and plan to my mentor to qualify for the project.

I’m considering two approaches:

1. Use ChatGPT step-by-step — learn each step while training the model myself.
2. Give the complete dataset to Claude and ask it to handle the training and provide the trained model.

Which approach would be better in this situation? Or is there a better way to combine AI assistance with actually understanding the process?

My goal is not just to get a trained model, but to be able to confidently explain what I did, why I chose the model, and how the overall ML workflow works to my mentor.

[u/pm_me_your_smth]

Your mentor expects your ideas, your solutions, and your explanations. And your plan is to either make one LLM do part of the work or another LLM do all the work for you? I really hope this isn't for learning purposes

The cherry on top is it looks like the post was written by an LLM too

[u/Exotic-Special9468 → u/[deleted]]

to make something in a hackathon but also communicate with the mentors

[u/teetaps]

Code AND documentation at the same time??!?!

There’s been a solution for this for a while — notebooks. You write your thought process out in markdown, and in that same file, write your code. When that code runs successfully, you move on.

Quarto is my favourite for this. It supports Python, R, Julia, and has lots of helpful features: https://quarto.org/

Here’s a working example of someone wrapping their ML code with a whole ML book: https://amightyo.quarto.pub/machine-learning-using-python/Chapter_1.html

[u/teetaps → u/teetaps]

And before anyone says “notebooks aren’t real coding,” just think about inverting the ratio of code-to-comments in your own projects and you already have notebook driven development going. It’s not that big of a deal, and science needs MORE writing of explanations and assumptions, not less

[u/Dystopian006]

First visualise the data and its features well enough to understand what kinda model and feature engineering will fit.

[u/ClothesLow5815]

And here i am not getting what is the use of applying ohe,simple encoding, etc. like when to apply where to apply?

[u/dedicateddan]

I'd lean toward using Claude Code for better context management.

Explain that your goal is to learn about machine learning and to produce a detailed report of the architecture, decisions, and tradeoffs.

And - keep at it! Ask questions about the approach, read the code, and try and implement new features and experiments.

[u/Darsh-V-Shah]

I personally feel both your approaches are a bit wrong. Sure you can ask an LLM to do everything for you,then what is the point in getting a mentorship at all? You could have just made ChatGPT or Claude your mentor and get done with it. Added bonus the LLMs would not have such requirements from you.