Unverified50% confidenceFactExact time
Concept drift refers to the statistical relationship between input data and the target variable changing over time, causing a previously trained model to gradually become ineffective; it is usually divided into three types: abrupt, gradual, and periodic.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
Related Entities
Related Claims
Verified模型漂移通常分为数据漂移和概念漂移两类，数据漂移指输入数据统计分布随时间变化，概念漂移指输入与输出关系本身发生变化78% similarUnverified模型漂移指模型上线后由于真实世界数据分布变化导致预测性能随时间下降的现象，应对手段包括持续监控、性能告警阈值和定期或触发式再训练。75% similarUnverified概念漂移（Concept Drift）指输入变量与目标变量之间的映射关系本身发生变化74% similarUnverifiedIn machine learning theory, data distribution shift is subdivided into three categories: covariate shift, label shift, and concept drift74% similarUnverifiedStock price series are typical non-stationary data, and the substantial change between the data distribution at model training time and at prediction time is referred to in statistics as 'concept drift'.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1017185API
curl https://kongchang.com/api/v1/knowledge/claims/1017185MCP
get_claim(id=1017185)