Research map / Learning theory and algorithms / Kernels and regression theory
A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use random hypothesis subspace sampling (RHSS) with a linear least squares combination of k sampled hypotheses | kernel ridge regression · Crime and Community | RMSE | No clear effect |
| Use random tree sampling (extra-tree style on bootstrap samples) with optimal linear weights | decision tree · Adult / COMPAS / Crime and Community | RMSE | Helped |
| Random projection as preprocessing before RHSS-KRR (ablation) | kernel ridge regression · Crime and Community | training time | Hurt |
| Change hypothesis sampling distribution (Gaussian, uniform, Laplace, Bernoulli) for RHSS-KRR (ablation) | RHSS-KRR · Crime and Community | RMSE | No clear effect |
| Increase number of hidden neurons for RHSS-MLP with k fixed at 100 (ablation) | RHSS-MLP · Crime and Community | RMSE | No clear effect |
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