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Related Questions
- What are the key differences between online and offline learning in the context of concept drift?
- Can you explain how to detect concept drift using statistical methods such as cumulative sum (CUSUM) or exponentially weighted moving average (EWMA)?
- How can I update a machine learning model to adapt to concept drift using incremental learning or online learning techniques?
- What are some strategies for handling concept drift in real-time systems, such as IoT sensor data or financial transactions?
- Can you discuss the role of data preprocessing and feature engineering in mitigating the effects of concept drift?
- How can I evaluate the performance of a machine learning model in the presence of concept drift using metrics such as accuracy, precision, or F1 score?
- What are some common pitfalls to avoid when handling concept drift, such as overfitting or underfitting, and how can I mitigate them?
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