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This work compares k-nearest neighbors and decision tree classifiers on high-dimensional prostate cancer gene expression data, focusing on test error, model complexity, and generalization. The study demonstrates the bias–variance tradeoff and shows that moderately complex models outperform both overly flexible and overly simplified alternatives
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