Sander Dieleman on X: The way overfitting is usually taught: you underfit for a while, then at some point, you start overfitting. This phase transition perspective can be misleading. As Alex points

Overfitting vs Underfitting in Machine Learning: Everything You Need to Know

Overfitting and Underfitting in Machine Learning, by Yash Goel

Overfitting and Underfitting Principles in Machine Learning

Model Selection: Underfitting, Overfitting, and the Bias-Variance Tradeoff

Dr. Matias Valdenegro Toro🐧 (@mvaldenegro) / X

OPML#0] A series of posts on over-parameterized machine learning models

Lasse Elsemüller (@elseml_) / X

Overfitting and Underfitting Principles in Machine Learning

Justin Bayer (@usuallyuseless) / X