Target drift occurs when the underlying goal or objective of the model changes. This can happen when the definition of the target variable or the criteria for classification or regression shift. For example, in a credit risk assessment model, target drift might occur if the lender’s risk tolerance or credit scoring criteria change.
Model drift refers to the degradation of a model’s performance due to changes in the environment or data distribution. This can be caused by changes in data quality, feature distributions, or the relationships between variables. Model drift can occur due to various reasons such as:
Data drift occurs when the distribution of the input data changes over time. This can be due to various reasons such as:
Concept drift occurs when the underlying relationships between variables or the underlying concept itself changes. This can happen when:
To mitigate drift, it’s essential to monitor model performance over time, detect changes in data distributions, and update or retrain models as needed. This can involve techniques such as:
By understanding and addressing drift, machine learning practitioners can ensure that their models remain accurate and effective over time, even in the face of changing data and environments.