Resilience
Resilience in Data Science refers to the ability of a system or model to adapt and recover from unexpected events or changes in the environment. It is the capacity to continue functioning even when faced with challenges such as data loss, system failures, or changes in user behavior. Resilience is achieved through the use of techniques such as redundancy, fault tolerance, and disaster recovery planning. In machine learning, resilience can be improved by using techniques such as regularization, ensemble learning, and outlier detection to make models more robust to noise and outliers in the data. Resilience is an important aspect of data-driven decision making, as it ensures that models and systems can continue to provide accurate and reliable results even in the face of unexpected events.
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