What is Drift In Machine Learning?
Drift is defined as the change in the data over time. It also means the change in the properties of the target variable, due to unpredictable or unforeseen changes, over the due course of time.
Data drift can be described as the change in the distribution of data, between the real-time data and the baseline data that was predicted or set beforehand.
Concept drift is the change between the relationship between input and the output given in any situation.
Drift can be in any form. It can be gradual, recurring, or sudden. It can be a positive or negative drift. The change in data over time can affect model outcomes, making drift an important metric to monitor when it comes to model performance.