Your question is Tree-Based Model Differences. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do Decision Trees, Random Forest, and XGBoost differ in how they learn and what tradeoffs they make?
Using a labeled tabular dataset, implement and compare all three models. Discuss their training procedures, bias and variance, interpretability, computational cost, sensitivity to noise, and appropriate use cases. Evaluate the models with consistent validation and explain which model you would select based on the results.