Decision Trees and Ensemble Methods Questions
How decision trees recursively split data. Hyperparameters: max depth, min samples split, criterion. Ensemble methods: random forests, gradient boosting. Understanding why ensembles work (combining weak learners). Trade-offs: complexity, interpretability, bias-variance. When to use trees vs. linear models.
HardTechnical
39 practiced
For a revenue forecasting task, a linear model and a tree ensemble have similar offline error, but the linear model retrains faster, is easier to monitor, and produces stable coefficients that the finance team already trusts. Under what circumstances would you still choose the tree ensemble, and when would you intentionally avoid it?
MediumTechnical
49 practiced
A boosted tree model is being used to trigger manual reviews, but the scores are poorly calibrated and the operations team relies on the exact probability to set staffing thresholds. What would you check before deployment, and how would you make the scores more trustworthy?
MediumTechnical
71 practiced
A gradient boosting model starts to overfit after a few dozen trees, even though each individual tree is shallow. What training choices would you adjust before switching to a different algorithm, and in what order would you try them?
MediumTechnical
42 practiced
In a risk scoring project, two different samples from the same population produce very different tree structures and split points. What does that tell you about the model, and what would you change if you needed a more stable score in production?
MediumTechnical
53 practiced
A decision tree on a fraud dataset fits the training data almost perfectly but loses a lot of accuracy on validation. Which tree-growth settings would you inspect first, and how would each one change the tree's behavior?
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