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Addressing Implementation-Specific Confounding Variables in Classification Models

The author faces a challenge in developing a classification model due to unidentifiable, group-based confounding variables. Despite achieving high accuracy with a random forest model, the performance drops significantly when tested with leave-one-group-out (LOGO) method, indicating the presence of implementation-specific variables that affect model performance.

Computing & Technology, Computer Science, Machine Learning