@aeon-actions-bot assign @aryan0931

Hey @MatthewMiddlehurst, I am keen in solving this documentation issue. Can I please be assigned this?

What do you plan to add?

Hi @MatthewMiddlehurst , I have experience in scientific computing and AI-driven projects, and I'm familiar with both sklearn and documentation structuring. I can enhance the examples to make it easier for sklearn users to get started with aeon. Let me know if I can take this up .

What do you plan on adding?

Thanks for the clarification! After reviewing the documentation, I noticed that while it covers classification (using KNeighborsClassifier) and clustering (DBSCAN), there are still some areas where more clarity could be helpful. I’d like to contribute by adding:
Regression Examples: Right now, there aren’t any concrete examples of using aeon distances with scikit-learn regression models. I plan to add a practical example demonstrating how to integrate aeon distances in a regression workflow.

Better Data Formatting Guidance: The docs mention the difference between aeon’s 3D format (n_cases, n_channels, n_timepoints) and sklearn’s 2D format but don’t provide step-by-step instructions on converting between the two. I’d like to add clear examples of how to reshape data correctly.

Preprocessing Tips: Since sklearn expects 2D arrays, preprocessing is crucial when working with time-series data. I’ll include guidance on structuring datasets properly, ensuring compatibility, and avoiding common errors.

Ok feel free to open a PR and we can see how it looks. I wouldn't close this issue in it though.

I've opened a PR for this. Looking forward to your feedback

Hi @MatthewMiddlehurst
This is a great point — I’ve also found myself wishing for more sklearn-style examples when getting started with aeon. I’ve got some experience with sklearn and would love to help out with this!

I’m happy to put together a “getting started for sklearn users” section with examples for clustering, classification, regression, and cross-validation. Let me know if that sounds good to you!