Research map / Learning theory and algorithms / Sample complexity
On Coresets for Logistic Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| sample by sqrt l2 leverage scores mixed with uniform instead of uniform sampling | coreset sampling for logistic regression · WEBB SPAM, COVERTYPE, KDD CUP '99 | mean relative log-likelihood error | Helped |
| QR sampling instead of k-means sensitivity sampling | coreset sampling for logistic regression · WEBB SPAM, COVERTYPE, KDD CUP '99 | mean relative log-likelihood error | Helped |
| QR sampling versus competitors at fixed time budget, large-error regime | coreset sampling for logistic regression · WEBB SPAM, COVERTYPE | error vs running time: QR better by a factor between 1.5-3 at larger budgets | Mixed |
| QR sampling on KDD CUP '99 (high mu) | coreset sampling for logistic regression · KDD CUP '99 | mean relative log-likelihood error: competitors above 15% vs QR never exceeds 22% and drops below 4% | Helped |
| QR sampling running time versus uniform and k-means | coreset sampling for logistic regression · all three datasets | relative running time | No clear effect |
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