How do Decision Trees behave under concept drift in production systems?

Decision Trees degrade under concept drift because learned splits become outdated as data distribution changes.

Concept drift occurs when the statistical properties of the target variable change over time. Since decision trees are trained on historical data, their static structure becomes misaligned with new patterns. Splits that were once optimal may lose predictive power. Unlike online learning models, standard trees do not adapt incrementally. To handle drift, systems use periodic retraining, sliding windows, or ensemble refresh strategies where newer trees replace older ones. Drift detection methods can also trigger retraining automatically.