Research map / Probabilistic and scientific modeling / Variational and generative sampling
Joints in Random Forests
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GeF with LearnSPN-learned leaf densities instead of fully factorised leaves (ablation) | GeF · 21 classification datasets, 30% test-time missing values | accuracy | Mixed |
| Marginalise missing features using GeF rather than impute them with KNN | Random Forest · 21 classification datasets, varying test-time missingness | accuracy: more than 3% | Helped |
| Use GeF instead of built-in surrogate splits or Friedman's method | Random Forest · 21 classification datasets, 30% test-time missing values | accuracy | Helped |
| Combine GeDT joint distributions as a uniform mixture to form GeF+ (ablation) | GeF · Classification with missing data | classification performance | Hurt |
| Use GeF+ log-density as an outlier score | GeF+ · Wine datasets; MNIST tested against Semeion and SVHN | AUC ROC | Helped |
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