Imagine a scenario where a machine learning model responsible for financial fraud detection starts generating a significantly higher number of false positives. What could be a plausible explanation for this sudden shift?
- Data drift.
- Model overfitting.
- Hardware malfunction.
- Incorrect algorithm choice.
Data drift is a plausible explanation for an increase in false positives. Data distribution can change over time, making the model's training data less representative of real-world data, leading to a drop in performance.
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