#93: Scikit-learn 90:Supervised Learning 68: Probability Calibration

The video discusses both intuition and code for Probability Calibration in Scikit-learn in Python. Includes: .calibration_curve(), .CalibrationClassifierCV(), .brier_score_loss(), .precision_score(), .recall_score(), .f1_score() Timeline (Python 3.8) 00:00 - Outline of video 00:30 - What is Probability Calibration? 03:34 - Example: n=10 04:46 - * * * CORRECTION * * *: meant to say '0.1 to 0.2' instead of '0.3' 07:42 - Example: n=100 09:11 - Calibrated vs. Uncalibrated 09:29 - How to calibrate? 11:03 - Code snippet 12:24 - Open Jupyter notebook 12:46 - Data 14:01 - Calibration with prior fit or prefit 29:25 - * * * CORRECTION * * * it should be 'y_pred_prob' in place of 'y_pred_base_prob' and not 'y_pred'. Corrected later at '30:10' 31:28 - Calibration without prefit 34:26 - Ending notes