Research map / Probabilistic and scientific modeling / Bayesian optimization
Optimization over Continuous and Multi-dimensional Decisions with Observational Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add uncertainty penalization to random forest | Random forest pricing policy · Synthetic pricing | expected revenue: $270 | Helped |
| Add uncertainty penalization to CART | CART dosing policy · Warfarin dosing | test-set MSE: 2.2% | Helped |
| Add uncertainty penalization to random forest | Random forest dosing policy · Warfarin dosing | test-set MSE: 8.6% | Helped |
| Add uncertainty penalization to Lasso | Lasso dosing policy · Warfarin dosing | test-set MSE: 0.5% | Helped |
| Use the extreme tuning choice λ1=100, λ2=0 (ablation) | Uncertainty-penalized random forest dosing · Warfarin dosing | out-of-sample error: λ1=100, λ2=0 | Hurt |
| Use uncertainty penalization rather than direct predicted-cost minimization (ablation) | Random forest pricing policy · Synthetic pricing | expected revenue: $270 | Helped |
| Use uncertainty penalization rather than unpenalized prediction (ablation) | CART, random forest, and Lasso dosing policies · Warfarin dosing | test-set MSE: 2.2%, 8.6%, 0.5% | Helped |
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