Probability Correction After Resampling: Training vs Real Prevalence & a Calibration Table
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Lessons in this course
- The Accuracy Trap: 3 Faults in 100 Runs, Confusion Counts & the «Always OK» Model Pro
- Metrics That Respect the Minority: Balanced Accuracy, the Matthews Coefficient, F1 & Fβ Pro
- Random Undersampling & Oversampling: Given Draw Tables, New Class Shares & What Gets Lost Pro
- Synthetic Minority Samples: Neighbour Distances, the k Nearest & New Points by Interpolation Pro
- Class Weights: n ÷ (k × n_class), a Weighted Log-Loss & a New Best Cut-Off Pro
- Cost-Based Thresholds: A Cost Matrix, Expected Cost per Run & the Cheapest Cut-Off Pro
- Probability Correction After Resampling: Training vs Real Prevalence & a Calibration Table Pro
- Capstone – Comparing Strategies: Balanced Accuracy, MCC, Recall, Expected Cost & a Ranked Summary Pro