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classifier balancing

classifier balancing

Mar 01, 2021 · One reason to avoid “balancing” your imbalanced training data is that such methods bias/distort the resulting trained model’s probability predictions so that these become miscalibrated, by systematically increasing the model’s predicted probabilities of the original minority class, and are thus reduced to being merely relative ordinal discriminant scores or decision functions or confidence …

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