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YouTubeProfessor Py: AI Foundations
Calibrate After Resampling in Python: Fix Probabilities for Imbalanced Data
Resampling helps rare classes but breaks probability meaning — calibration with scikit-learn restores valid scores before you set thresholds. Learn a practical workflow to oversample for ranking, fit a calibrated wrapper to map scores back to the base rate, and choose data-driven thresholds (e.g., top 10% review). Demo uses scikit-learn and ...
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