Continuous Weight Balancing
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We propose a simple method by which to choose sample weights for problems with highly imbalanced or skewed traits. Rather than naively discretizing regression labels to find binned weights, we take a more principled approach – we derive sample weights from the transfer function between an estimated source and specified target distributions. Our method outperforms both unweighted and discretely-weighted models on both regression and classification tasks. We also open-source our implementation of this method (https://github.com/Daniel-Wu/Continuous-Weight-Balancing) to the scientific community.
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