AI Paper Atlas

Research map / Learning theory and algorithms / Prediction and decision losses

Stability of Random Forests and Coverage of Random-Forest Prediction Intervals

NeurIPS 2023 · Learning theory and algorithms › Prediction and decision losses · approach: Probabilistic calibration

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
relax the light-tail assumption on Y^2 to a standard Cauchy responserandom forest stability · Synthetic Cauchy-response data (n=4000, B=1000)stability |RF_B - RF_B^(-i)|: epsilon-hat 0.237 at nu-hat 0.05No clear effect
compare OOB errors with test prediction errorsRF out-of-bag prediction interval · Synthetic Cauchy-response data (n=4000, B=1000)error distributionHelped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy