Concept Drift

Data drift or concept drift refers to the phenomenon where model performance degrades significantly when the real-world data distribution changes while the model is still trained on old data; the industry addresses this with continuous learning and active learning strategies.

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

Means of addressing concept drift include online learning, sliding window retraining, and drift detection algorithms (such as ADWIN and DDM).

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.

Distribution shift includes three common types: covariate shift (the input distribution changes but the conditional distribution does not), label shift (the label marginal distribution changes) and concept drift (the conditional distribution changes over time).

Concept drift refers to the mapping relationship between input data and target labels changing over time, which is different from data drift where only the input distribution changes.

Data drift and concept drift can cause model performance degradation, but may show no anomalies in service-layer health metrics (latency, throughput, error rate), requiring active detection through statistical methods such as KL divergence, PSI, and KS tests.

Data distribution drift falls into three types—covariate shift, prior probability shift, and concept drift—of which concept drift is the most insidious and dangerous.

Dataset shift primarily includes three subtypes: covariate shift (changes in the distribution of input features while the conditional label distribution remains unchanged), prior probability shift (changes in the marginal distribution of labels), and concept drift (changes in the mapping relationship between inputs and outputs)

Stock 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'.

Financial fraud is an adversarial game scenario where fraud patterns continuously drift, leading to concept drift.

Financial fraud is an adversarial game scenario in which the prior probability and conditional probability distributions the model relies on continuously drift, a phenomenon known as Concept Drift

Financial fraud is an adversarial game scenario where fraud patterns continuously drift, leading to the concept drift problem.

概念漂移可分为突变型（sudden）、渐变型（gradual）、增量型（incremental）和周期型（recurring）等类型