ML 20: Same Accuracy, Different Answers

Disclosure: this post was written with the assistance of an AI system (Claude), which wrote the analysis code, ran the experiments and drafted the text. The topic, the constraints, the data choices and the final review are the author’s. On a simulated task where the best possible rule errs on 27.6% of the 5,000 test rows, five neural networks that differ only in their random seed score between 0.670 and 0....

October 12, 2026 · 9 min · Jongha Jeon

ML 57: The BIC Counts Gaussians, Not Clusters

Disclosure: this post was written with the assistance of an AI system (Claude), which wrote the analysis code, ran the experiments and drafted the text. The topic, the constraints, the data choices and the final review are the author’s. Gaussian mixtures fitted to a single two-dimensional cluster: for a Gaussian one, the BIC chose one component at every size from 200 to 20,000 points; for a uniform square it chose 2, 7, 14, 18; for a heavy-tailed cluster 2, 3, 4, 9....

October 14, 2026 · 9 min · Jongha Jeon

ML 60: The Most Stable k Is Not the Number of Clusters

Disclosure: this post was written with the assistance of an AI system (Claude), which wrote the analysis code, ran the experiments and drafted the text. The topic, the constraints, the data choices and the final review are the author’s. k-means stability, the agreement between clusterings of two random 80% subsamples, chose k = 2 for one elongated Gaussian (stability 0.92) and k = 4 for a uniform square (0.94), neither of which has clusters....

October 14, 2026 · 9 min · Jongha Jeon