Research map / Probabilistic and scientific modeling / Molecules and proteins
Automatic Discovery of the Statistical Types of Variables in a Dataset
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| infer data types per attribute vs fixing real-valued/categorical | latent feature model · six of seven UCI datasets | test log-likelihood per observation | Helped |
| infer data types vs baseline | latent feature model · Student | test log-likelihood per observation | Hurt |
| vary number of latent features K from 2 to 10 | proposed model and baseline · real datasets | test log-likelihood | No clear effect |
| vary number of latent variables K from 1 to 10 (ablation) | proposed model · synthetic discrete data | type identification | No clear effect |
| decrease number of categories R in discrete variable (ablation) | proposed model · synthetic categorical vs ordinal | type identification | Hurt |
| increase R in ordinal data (ablation) | proposed model · synthetic ordinal data | type identification | Mixed |
| heavier tail of Beta distribution (ablation) | proposed model · synthetic interval data | likelihood weights | Mixed |
| Gaussian vs Gamma ground truth (ablation) | proposed model · synthetic continuous data | weight on interval type | Mixed |
| German duration modeled as count | proposed model · German | inferred type | Mixed |
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