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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