Feb. 15, 2024, 8:07 a.m. | /u/philosophicalmachine

Machine Learning www.reddit.com

I'm working in a field where datasets are typically small (100-10000 samples) and hierarchical (taken from 10-50 participants). This means that in order to evaluate the data on a large enough testing set with more than just a handful of participants, we need to use cross-validation. So far so good.

However, this still leaves the validation unresolved. There are several possible approaches to do the validation:

1. Skip the validation. This seems to be the preferred approach in my field. …

data datasets good hierarchical machinelearning samples set small testing validation

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Quality Intern

@ Syngenta Group | Toronto, Ontario, Canada