Why Do Models Fail in New Environments?
This study identifies three critical pitfalls in predictive modeling, including improper cross-validation estimators, data leakage during model selection, and neglecting experimental block effects. These issues significantly compromise the reliability of model evaluations in agricultural research. Furthermore, it provides a comprehensive analysis of how various performance metrics behave under different data distributions, offering guidelines for selecting appropriate indicators for both regression (addressing bias and variance) and classification (addressing class imbalance) tasks
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