Random Undersampling & Oversampling: Given Draw Tables, New Class Shares & What Gets Lost
This lesson is part of the course
In this lesson
You undersample the OK runs and oversample the fault runs with given draw tables, compute the new class shares and see how each method changes the data the model learns from.
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Lessons in this course
- The Accuracy Trap: 3 Faults in 100 Runs, Confusion Counts & the «Always OK» ModelFree
- Metrics That Respect the Minority: Balanced Accuracy, the Matthews Coefficient, F1 & Fβ Locked
- Random Undersampling & Oversampling: Given Draw Tables, New Class Shares & What Gets Lost Locked
- Synthetic Minority Samples: Neighbour Distances, the k Nearest & New Points by Interpolation Locked
- Class Weights: n ÷ (k × n_class), a Weighted Log-Loss & a New Best Cut-Off Locked
- Cost-Based Thresholds: A Cost Matrix, Expected Cost per Run & the Cheapest Cut-Off Locked
- Probability Correction After Resampling: Training vs Real Prevalence & a Calibration Table Locked
- Capstone – Comparing Strategies: Balanced Accuracy, MCC, Recall, Expected Cost & a Ranked Summary Locked