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Classroom Contents
Better Decisions with Machine Learning - Peter Flach
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- 1 Intro
- 2 Decisions, decisions..
- 3 Outline of the talk
- 4 Majority class decision rule
- 5 Adapting to deployment context 17-1/31
- 6 1. Introducing ROC curves and calibration
- 7 Logistic calibration from first principles
- 8 Example of inverse-sigmoidal distortion
- 9 Beta calibration from first principles
- 10 A rich parametric family
- 11 Beta-calibration is easily implemented
- 12 Precision-Recall-Gain Curves
- 13 Model calibrated for F-score In-1/21
- 14 ROC curves and Precision-Recall curves
- 15 Properties of ROC curves
- 16 F-score calibration
- 17 Perspective: Towards a measurement theory for ML
- 18 Measuring things
- 19 Concatenation and scales
- 20 Concatenating confusion matrices
