Decision Tree Instability

Model Robustness

Decision Tree Instability

Small data changes, large boundary changes.

What This Shows

Observe how small perturbations in the training data can change a decision tree. The applet captures a practical issue in interpretable modeling: simple rules can be easy to explain, but their apparent clarity may hide sensitivity to sampling noise.

Decision trees Instability Validation Robustness Interpretability

Technical Challenge

Separating interpretable structure from unstable artifacts of a specific sample.

My Contribution

Built a focused visualization of tree boundary sensitivity under small data changes.

Industry Transfer

Credit policies, churn models, operational rules, interpretable ML governance.

Connected Work

Model validation and production experimentation.