Class-Imbalance on The Standard Errorhttps://jongha-jeon-dev.github.io/standarderror/tags/class-imbalance/Recent content in Class-Imbalance on The Standard ErrorHugo -- 0.123.7en-gbThu, 08 Oct 2026 00:00:00 +0000ML 5: Resampling Moves the Threshold and Breaks a Logistic Model's Probabilitieshttps://jongha-jeon-dev.github.io/standarderror/lectures/ml-5-imbalance/Thu, 08 Oct 2026 00:00:00 +0000https://jongha-jeon-dev.github.io/standarderror/lectures/ml-5-imbalance/'Balance the classes before training' is the first fix taught for a rare positive class. Measured, it never improved the ranking, multiplied a logistic model's probabilities tenfold, and did nothing to its decisions that a threshold does not. Boosted trees lost ranking and, with some methods, gained calibration.