Unverified50% confidenceFactExact time
The excess risk bound measures the risk gap between the predictor obtained by the learning algorithm and the optimal predictor within the hypothesis space, and typically consists of two parts: estimation error and approximation error.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedProbabilistic guarantee algorithms output results satisfying an error bound with high probability (such as 1-δ), but carry a small probability of failure, which is fundamentally different from the deterministic error analysis of traditional numerical algorithms.73% similarUnverifiedTotal prediction error can be decomposed into bias² + variance + irreducible error.72% similarUnverifiedInterval forecasting provides an uncertainty range for the predicted value, and is more practical than point forecasting for highly volatile B2B businesses.72% similarUnverifiedA learner estimating the loss distribution from limited data inevitably has bias, causing the estimate of CVaR to deviate from the true value, thereby weakening or even reversing the actual effect of risk aversion.71% similarUnverifiedThe expected error of a supervised learning model on unseen data can be decomposed into three components: bias², variance, and irreducible noise71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1022988API
curl https://kongchang.com/api/v1/knowledge/claims/1022988MCP
get_claim(id=1022988)