机器学习之概念漂移-concept drift

1. 概念漂移(concept drift)

　　背景：概念漂移指的是数据流中的潜在数据分布随时间发生不可预测的变化，使原有的分类器分类不准确或决策系统无法正确决策，常见于推荐系统、金融领域、决策等

　　　　Concept drift refers to unforeseeable changes in the underlying data distribution of data streams over time. 

　　定义：Concept drift in machine learning and data mining refers to the change in the relationships between input and output data in the underlying problem over time. (https://machinelearningmastery.com/gentle-introduction-concept-drift-machine-learning/)

　　我的理解：目标函数target随时间发生不可预测性变化。比如：input(x1) --> target(x1) 概念漂移: input(x1) --> target(x2).

2. 概念漂移检测(concept drift detection method)

　　待整理。