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Partial Dependence Plot (PDP) on IEEE-CIS

3.3Runtimescikit-learn
-1.06428.39357.8587.307May 14, 2026
Updated 5mo ago

Evaluation results

MethodLinks
scikit-learnComputation Type=Approximate, k=52026.053.3
WOODELF++Computation Type=Approximate, k=5, Hardware=CPU2026.054.6
WOODELF++Computation Type=Approximate, k=5, Hardware=GPU2026.055.4
WOODELF++Computation Type=Exact, k=full, Hardware=GPU2026.057
WOODELF++Computation Type=Exact, k=100, Hardware=GPU2026.059
WOODELF++Computation Type=Exact, k=10, Hardware=GPU2026.0510
WOODELF++Computation Type=Exact, k=full, Hardware=CPU2026.0511.8
WOODELF++Computation Type=Exact, k=5, Hardware=GPU2026.0513.1
WOODELF++Computation Type=Exact, k=5, Hardware=CPU2026.0514.5
WOODELF++Computation Type=Exact, k=100, Hardware=CPU2026.0514.8
WOODELF++Computation Type=Exact, k=10, Hardware=CPU2026.0515.2
scikit-learnComputation Type=Exact, k=1002026.0519
scikit-learnComputation Type=Exact, k=52026.0558.5
scikit-learnComputation Type=Exact, k=full2026.0559.3
FastPDComputation Type=Exact, k=full2026.0598
FastPDComputation Type=Exact, k=52026.0599
FastPDComputation Type=Exact, k=102026.0599
FastPDComputation Type=Exact, k=1002026.0599
scikit-learnComputation Type=Exact, k=102026.05112.4