Overfitting, regularization and interpretation
An overly flexible model can fit noise in training data and fail on new data.
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An overly flexible model can fit noise in training data and fail on new data. Regularization limits complexity; a baseline shows whether complexity helps. Feature importance can identify useful predictors but does not prove a molecular mechanism. Compare train and validation performance, and be explicit about the population represented by the data.
Worked example
A tree with 100% training accuracy and 58% held-out accuracy is overfitting. Compare a shallow tree and majority-class baseline before interpreting its selected genes.