ML 41: An Average of Models Beats the Average Model, Not the Best One

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. Six classifiers on the digits, and the averaged probabilities of all 57 combinations of two or more. 11 combinations beat their best member’s accuracy and 8 fell below their members’ mean. Averaged with a single decision tree, whose probabilities are all 0 or 1, an SVM scoring 0....

October 13, 2026 · 9 min · Jongha Jeon

ML 42: Stacking Learns to Pick the Best Model, Given Honest Predictions

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. Four base models on a simulated task, 100 to 3,000 training rows, and a logistic meta-model on their probabilities. Fitted on in-sample predictions it gave 1-NN a weight of 13 and reproduced its accuracy exactly (0....

October 13, 2026 · 9 min · Jongha Jeon