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Recalibration of Predictive Models
Recalibration adjusts the output probabilities of a machine learning model to ensure they accurately reflect the true likelihood of an event, even if the initial model's raw scores are poorly scaled. For instance, if a model predicts a 70% chance of rain, recalibration ensures that it actually rains approximately 70% of the times the model makes that prediction. This is critical for decision-making based on probabilities.
In plain terms
It's like tuning a musical instrument so that the notes played perfectly match their intended pitch.
Why it matters
Ensures that model probabilities are trustworthy and actionable, crucial for cost-sensitive decisions or risk assessment.
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