Data Drift
Data Drift refers to the phenomenon where the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This can be caused by various factors such as changes in user behavior, changes in the underlying data generation process, or changes in the external environment. Data Drift can lead to a decrease in model performance and accuracy over time, as the model is no longer able to accurately capture the underlying patterns in the data. To mitigate Data Drift, it is important to continuously monitor the performance of the model and retrain it on new data as necessary.
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