How to Evaluate a Machine Learning Model
A practical guide to precision, recall, F1, ROC-AUC, and calibration, and how to choose evaluation metrics that reflect what mistakes actually cost.
8 min read
The archive
Machine Learning
Research results and the fundamentals behind the models.
Supervised, Unsupervised, and Self-Supervised Learning: A Practical Comparison
How the three main ways of learning from data differ, what each one needs from you, and how to pick the right approach for a real project.
9 min read