Research map / Learning theory and algorithms / Prediction and decision losses
A Communication-Efficient Parallel Algorithm for Decision Tree
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use PV-Tree's local and global voting to limit histogram exchange | parallel decision tree training · LTR | convergence time (seconds): 32260 -> 5825 | Helped |
| use PV-Tree's local and global voting to limit histogram exchange | parallel decision tree training · CTR | convergence time (seconds): 9209 -> 5349 | Helped |
| use PV-Tree rather than attribute-parallel training | parallel decision tree training · LTR | convergence time (seconds): 14660 -> 5825 | Helped |
| use PV-Tree rather than attribute-parallel training | parallel decision tree training · CTR | convergence time (seconds): 26928 -> 5349 | Helped |
| use PV-Tree instead of quantized-histogram data-parallel training | GBDT training · LTR and CTR | convergent accuracy | Helped |
| increase the voting parameter from k=1 to k=5 (ablation) | PV-Tree · LTR, M=4 | NDCG: 0.7905 -> 0.7909 | Helped |
| increase the voting parameter from k=1 to k=40 (ablation) | PV-Tree · LTR, M=16 | NDCG: 0.7882 -> 0.7909 | Helped |
| increase the number of machines from 8 to 16 (ablation) | PV-Tree · LTR | convergence speed | Hurt |
| use more machines, reducing local training data per machine (ablation) | PV-Tree · fixed-size training data | voting accuracy | Mixed |
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