Long-term Retention
Long-term retention refers to the ability of a machine learning model to retain its performance over a long period of time. In other words, it is the ability of a model to maintain its accuracy and effectiveness when presented with new data that it has not seen before. Long-term retention is important in many real-world applications, such as fraud detection, recommendation systems, and predictive maintenance, where the model needs to be able to adapt to changing conditions and new patterns in the data. To achieve long-term retention, the model needs to be trained on a diverse and representative dataset, and the training process should include techniques such as regularization, early stopping, and data augmentation. Additionally, the model should be periodically retrained on new data to ensure that it continues to perform well over time.
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